Add new SentenceTransformer model
Browse files- 1_Pooling/config.json +10 -0
- README.md +2730 -141
- config.json +1 -1
- config_sentence_transformers.json +10 -0
- configuration.py +145 -0
- modules.json +14 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +37 -0
- tokenizer.json +0 -0
- tokenizer_config.json +62 -0
- vocab.txt +0 -0
1_Pooling/config.json
ADDED
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@@ -0,0 +1,10 @@
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{
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"word_embedding_dimension": 768,
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"pooling_mode_cls_token": true,
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"pooling_mode_mean_tokens": false,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false,
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"include_prompt": true
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}
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README.md
CHANGED
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@@ -1,199 +1,2788 @@
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---
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library_name: transformers
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-
tags:
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| 4 |
---
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| 5 |
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| 6 |
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#
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| 7 |
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| 8 |
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| 10 |
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| 11 |
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| 12 |
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| 13 |
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| 14 |
-
### Model Description
|
| 15 |
|
| 16 |
<!-- Provide a longer summary of what this model is. -->
|
| 17 |
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| 18 |
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| 19 |
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| 20 |
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- **
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| 21 |
-
- **Funded by [optional]:** [More Information Needed]
|
| 22 |
-
- **Shared by [optional]:** [More Information Needed]
|
| 23 |
-
- **Model type:** [More Information Needed]
|
| 24 |
-
- **Language(s) (NLP):** [More Information Needed]
|
| 25 |
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- **License:** [More Information Needed]
|
| 26 |
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- **Finetuned from model [optional]:** [More Information Needed]
|
| 27 |
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| 28 |
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### Model
|
| 29 |
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| 30 |
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| 31 |
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| 32 |
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- **Repository:** [More Information Needed]
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| 33 |
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- **Paper [optional]:** [More Information Needed]
|
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- **Demo [optional]:** [More Information Needed]
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##
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Training Details
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### Training Data
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### Training Procedure
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#### Training Hyperparameters
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[More Information Needed]
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## Evaluation
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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-
## Environmental Impact
|
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
|
| 144 |
-
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| 145 |
-
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
|
| 146 |
-
|
| 147 |
-
- **Hardware Type:** [More Information Needed]
|
| 148 |
-
- **Hours used:** [More Information Needed]
|
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-
- **Cloud Provider:** [More Information Needed]
|
| 150 |
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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-
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## Technical Specifications [optional]
|
| 154 |
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-
### Model Architecture and Objective
|
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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-
#### Software
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| 168 |
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[More Information Needed]
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| 170 |
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|
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-
## Citation [optional]
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-
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
|
| 174 |
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-
**BibTeX:**
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[More Information Needed]
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-
## Model Card Authors [optional]
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[More Information Needed]
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##
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| 1 |
---
|
| 2 |
library_name: transformers
|
| 3 |
+
tags:
|
| 4 |
+
- sentence-transformers
|
| 5 |
+
- gte
|
| 6 |
+
- mteb
|
| 7 |
+
- transformers.js
|
| 8 |
+
- sentence-similarity
|
| 9 |
+
license: apache-2.0
|
| 10 |
+
language:
|
| 11 |
+
- en
|
| 12 |
+
model-index:
|
| 13 |
+
- name: gte-base-en-v1.5
|
| 14 |
+
results:
|
| 15 |
+
- task:
|
| 16 |
+
type: Classification
|
| 17 |
+
dataset:
|
| 18 |
+
type: mteb/amazon_counterfactual
|
| 19 |
+
name: MTEB AmazonCounterfactualClassification (en)
|
| 20 |
+
config: en
|
| 21 |
+
split: test
|
| 22 |
+
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
|
| 23 |
+
metrics:
|
| 24 |
+
- type: accuracy
|
| 25 |
+
value: 74.7910447761194
|
| 26 |
+
- type: ap
|
| 27 |
+
value: 37.053785713650626
|
| 28 |
+
- type: f1
|
| 29 |
+
value: 68.51101510998551
|
| 30 |
+
- task:
|
| 31 |
+
type: Classification
|
| 32 |
+
dataset:
|
| 33 |
+
type: mteb/amazon_polarity
|
| 34 |
+
name: MTEB AmazonPolarityClassification
|
| 35 |
+
config: default
|
| 36 |
+
split: test
|
| 37 |
+
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
|
| 38 |
+
metrics:
|
| 39 |
+
- type: accuracy
|
| 40 |
+
value: 93.016875
|
| 41 |
+
- type: ap
|
| 42 |
+
value: 89.17750268426342
|
| 43 |
+
- type: f1
|
| 44 |
+
value: 92.9970977240524
|
| 45 |
+
- task:
|
| 46 |
+
type: Classification
|
| 47 |
+
dataset:
|
| 48 |
+
type: mteb/amazon_reviews_multi
|
| 49 |
+
name: MTEB AmazonReviewsClassification (en)
|
| 50 |
+
config: en
|
| 51 |
+
split: test
|
| 52 |
+
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
|
| 53 |
+
metrics:
|
| 54 |
+
- type: accuracy
|
| 55 |
+
value: 53.312000000000005
|
| 56 |
+
- type: f1
|
| 57 |
+
value: 52.98175784163017
|
| 58 |
+
- task:
|
| 59 |
+
type: Retrieval
|
| 60 |
+
dataset:
|
| 61 |
+
type: mteb/arguana
|
| 62 |
+
name: MTEB ArguAna
|
| 63 |
+
config: default
|
| 64 |
+
split: test
|
| 65 |
+
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
|
| 66 |
+
metrics:
|
| 67 |
+
- type: map_at_1
|
| 68 |
+
value: 38.193
|
| 69 |
+
- type: map_at_10
|
| 70 |
+
value: 54.848
|
| 71 |
+
- type: map_at_100
|
| 72 |
+
value: 55.388000000000005
|
| 73 |
+
- type: map_at_1000
|
| 74 |
+
value: 55.388999999999996
|
| 75 |
+
- type: map_at_3
|
| 76 |
+
value: 50.427
|
| 77 |
+
- type: map_at_5
|
| 78 |
+
value: 53.105000000000004
|
| 79 |
+
- type: mrr_at_1
|
| 80 |
+
value: 39.047
|
| 81 |
+
- type: mrr_at_10
|
| 82 |
+
value: 55.153
|
| 83 |
+
- type: mrr_at_100
|
| 84 |
+
value: 55.686
|
| 85 |
+
- type: mrr_at_1000
|
| 86 |
+
value: 55.688
|
| 87 |
+
- type: mrr_at_3
|
| 88 |
+
value: 50.676
|
| 89 |
+
- type: mrr_at_5
|
| 90 |
+
value: 53.417
|
| 91 |
+
- type: ndcg_at_1
|
| 92 |
+
value: 38.193
|
| 93 |
+
- type: ndcg_at_10
|
| 94 |
+
value: 63.486
|
| 95 |
+
- type: ndcg_at_100
|
| 96 |
+
value: 65.58
|
| 97 |
+
- type: ndcg_at_1000
|
| 98 |
+
value: 65.61
|
| 99 |
+
- type: ndcg_at_3
|
| 100 |
+
value: 54.494
|
| 101 |
+
- type: ndcg_at_5
|
| 102 |
+
value: 59.339
|
| 103 |
+
- type: precision_at_1
|
| 104 |
+
value: 38.193
|
| 105 |
+
- type: precision_at_10
|
| 106 |
+
value: 9.075
|
| 107 |
+
- type: precision_at_100
|
| 108 |
+
value: 0.9939999999999999
|
| 109 |
+
- type: precision_at_1000
|
| 110 |
+
value: 0.1
|
| 111 |
+
- type: precision_at_3
|
| 112 |
+
value: 22.096
|
| 113 |
+
- type: precision_at_5
|
| 114 |
+
value: 15.619
|
| 115 |
+
- type: recall_at_1
|
| 116 |
+
value: 38.193
|
| 117 |
+
- type: recall_at_10
|
| 118 |
+
value: 90.754
|
| 119 |
+
- type: recall_at_100
|
| 120 |
+
value: 99.431
|
| 121 |
+
- type: recall_at_1000
|
| 122 |
+
value: 99.644
|
| 123 |
+
- type: recall_at_3
|
| 124 |
+
value: 66.28699999999999
|
| 125 |
+
- type: recall_at_5
|
| 126 |
+
value: 78.094
|
| 127 |
+
- task:
|
| 128 |
+
type: Clustering
|
| 129 |
+
dataset:
|
| 130 |
+
type: mteb/arxiv-clustering-p2p
|
| 131 |
+
name: MTEB ArxivClusteringP2P
|
| 132 |
+
config: default
|
| 133 |
+
split: test
|
| 134 |
+
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
|
| 135 |
+
metrics:
|
| 136 |
+
- type: v_measure
|
| 137 |
+
value: 47.508221208908964
|
| 138 |
+
- task:
|
| 139 |
+
type: Clustering
|
| 140 |
+
dataset:
|
| 141 |
+
type: mteb/arxiv-clustering-s2s
|
| 142 |
+
name: MTEB ArxivClusteringS2S
|
| 143 |
+
config: default
|
| 144 |
+
split: test
|
| 145 |
+
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
|
| 146 |
+
metrics:
|
| 147 |
+
- type: v_measure
|
| 148 |
+
value: 42.04668382560096
|
| 149 |
+
- task:
|
| 150 |
+
type: Reranking
|
| 151 |
+
dataset:
|
| 152 |
+
type: mteb/askubuntudupquestions-reranking
|
| 153 |
+
name: MTEB AskUbuntuDupQuestions
|
| 154 |
+
config: default
|
| 155 |
+
split: test
|
| 156 |
+
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
|
| 157 |
+
metrics:
|
| 158 |
+
- type: map
|
| 159 |
+
value: 61.828759903716815
|
| 160 |
+
- type: mrr
|
| 161 |
+
value: 74.37343358395991
|
| 162 |
+
- task:
|
| 163 |
+
type: STS
|
| 164 |
+
dataset:
|
| 165 |
+
type: mteb/biosses-sts
|
| 166 |
+
name: MTEB BIOSSES
|
| 167 |
+
config: default
|
| 168 |
+
split: test
|
| 169 |
+
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
|
| 170 |
+
metrics:
|
| 171 |
+
- type: cos_sim_pearson
|
| 172 |
+
value: 85.03673698773017
|
| 173 |
+
- type: cos_sim_spearman
|
| 174 |
+
value: 83.6470866785058
|
| 175 |
+
- type: euclidean_pearson
|
| 176 |
+
value: 82.64048673096565
|
| 177 |
+
- type: euclidean_spearman
|
| 178 |
+
value: 83.63142367101115
|
| 179 |
+
- type: manhattan_pearson
|
| 180 |
+
value: 82.71493099760228
|
| 181 |
+
- type: manhattan_spearman
|
| 182 |
+
value: 83.60491704294326
|
| 183 |
+
- task:
|
| 184 |
+
type: Classification
|
| 185 |
+
dataset:
|
| 186 |
+
type: mteb/banking77
|
| 187 |
+
name: MTEB Banking77Classification
|
| 188 |
+
config: default
|
| 189 |
+
split: test
|
| 190 |
+
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
|
| 191 |
+
metrics:
|
| 192 |
+
- type: accuracy
|
| 193 |
+
value: 86.73376623376623
|
| 194 |
+
- type: f1
|
| 195 |
+
value: 86.70294049278262
|
| 196 |
+
- task:
|
| 197 |
+
type: Clustering
|
| 198 |
+
dataset:
|
| 199 |
+
type: mteb/biorxiv-clustering-p2p
|
| 200 |
+
name: MTEB BiorxivClusteringP2P
|
| 201 |
+
config: default
|
| 202 |
+
split: test
|
| 203 |
+
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
|
| 204 |
+
metrics:
|
| 205 |
+
- type: v_measure
|
| 206 |
+
value: 40.31923804167062
|
| 207 |
+
- task:
|
| 208 |
+
type: Clustering
|
| 209 |
+
dataset:
|
| 210 |
+
type: mteb/biorxiv-clustering-s2s
|
| 211 |
+
name: MTEB BiorxivClusteringS2S
|
| 212 |
+
config: default
|
| 213 |
+
split: test
|
| 214 |
+
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
|
| 215 |
+
metrics:
|
| 216 |
+
- type: v_measure
|
| 217 |
+
value: 37.552547125348454
|
| 218 |
+
- task:
|
| 219 |
+
type: Retrieval
|
| 220 |
+
dataset:
|
| 221 |
+
type: mteb/cqadupstack-android
|
| 222 |
+
name: MTEB CQADupstackAndroidRetrieval
|
| 223 |
+
config: default
|
| 224 |
+
split: test
|
| 225 |
+
revision: f46a197baaae43b4f621051089b82a364682dfeb
|
| 226 |
+
metrics:
|
| 227 |
+
- type: map_at_1
|
| 228 |
+
value: 30.567
|
| 229 |
+
- type: map_at_10
|
| 230 |
+
value: 41.269
|
| 231 |
+
- type: map_at_100
|
| 232 |
+
value: 42.689
|
| 233 |
+
- type: map_at_1000
|
| 234 |
+
value: 42.84
|
| 235 |
+
- type: map_at_3
|
| 236 |
+
value: 37.567
|
| 237 |
+
- type: map_at_5
|
| 238 |
+
value: 39.706
|
| 239 |
+
- type: mrr_at_1
|
| 240 |
+
value: 37.053000000000004
|
| 241 |
+
- type: mrr_at_10
|
| 242 |
+
value: 46.900999999999996
|
| 243 |
+
- type: mrr_at_100
|
| 244 |
+
value: 47.662
|
| 245 |
+
- type: mrr_at_1000
|
| 246 |
+
value: 47.713
|
| 247 |
+
- type: mrr_at_3
|
| 248 |
+
value: 43.801
|
| 249 |
+
- type: mrr_at_5
|
| 250 |
+
value: 45.689
|
| 251 |
+
- type: ndcg_at_1
|
| 252 |
+
value: 37.053000000000004
|
| 253 |
+
- type: ndcg_at_10
|
| 254 |
+
value: 47.73
|
| 255 |
+
- type: ndcg_at_100
|
| 256 |
+
value: 53.128
|
| 257 |
+
- type: ndcg_at_1000
|
| 258 |
+
value: 55.300000000000004
|
| 259 |
+
- type: ndcg_at_3
|
| 260 |
+
value: 42.046
|
| 261 |
+
- type: ndcg_at_5
|
| 262 |
+
value: 44.782
|
| 263 |
+
- type: precision_at_1
|
| 264 |
+
value: 37.053000000000004
|
| 265 |
+
- type: precision_at_10
|
| 266 |
+
value: 9.142
|
| 267 |
+
- type: precision_at_100
|
| 268 |
+
value: 1.485
|
| 269 |
+
- type: precision_at_1000
|
| 270 |
+
value: 0.197
|
| 271 |
+
- type: precision_at_3
|
| 272 |
+
value: 20.076
|
| 273 |
+
- type: precision_at_5
|
| 274 |
+
value: 14.535
|
| 275 |
+
- type: recall_at_1
|
| 276 |
+
value: 30.567
|
| 277 |
+
- type: recall_at_10
|
| 278 |
+
value: 60.602999999999994
|
| 279 |
+
- type: recall_at_100
|
| 280 |
+
value: 83.22800000000001
|
| 281 |
+
- type: recall_at_1000
|
| 282 |
+
value: 96.696
|
| 283 |
+
- type: recall_at_3
|
| 284 |
+
value: 44.336999999999996
|
| 285 |
+
- type: recall_at_5
|
| 286 |
+
value: 51.949
|
| 287 |
+
- task:
|
| 288 |
+
type: Retrieval
|
| 289 |
+
dataset:
|
| 290 |
+
type: mteb/cqadupstack-english
|
| 291 |
+
name: MTEB CQADupstackEnglishRetrieval
|
| 292 |
+
config: default
|
| 293 |
+
split: test
|
| 294 |
+
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
|
| 295 |
+
metrics:
|
| 296 |
+
- type: map_at_1
|
| 297 |
+
value: 28.538000000000004
|
| 298 |
+
- type: map_at_10
|
| 299 |
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value: 38.757999999999996
|
| 300 |
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- type: map_at_100
|
| 301 |
+
value: 40.129
|
| 302 |
+
- type: map_at_1000
|
| 303 |
+
value: 40.262
|
| 304 |
+
- type: map_at_3
|
| 305 |
+
value: 35.866
|
| 306 |
+
- type: map_at_5
|
| 307 |
+
value: 37.417
|
| 308 |
+
- type: mrr_at_1
|
| 309 |
+
value: 36.051
|
| 310 |
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- type: mrr_at_10
|
| 311 |
+
value: 44.868
|
| 312 |
+
- type: mrr_at_100
|
| 313 |
+
value: 45.568999999999996
|
| 314 |
+
- type: mrr_at_1000
|
| 315 |
+
value: 45.615
|
| 316 |
+
- type: mrr_at_3
|
| 317 |
+
value: 42.558
|
| 318 |
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- type: mrr_at_5
|
| 319 |
+
value: 43.883
|
| 320 |
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- type: ndcg_at_1
|
| 321 |
+
value: 36.051
|
| 322 |
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- type: ndcg_at_10
|
| 323 |
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value: 44.584
|
| 324 |
+
- type: ndcg_at_100
|
| 325 |
+
value: 49.356
|
| 326 |
+
- type: ndcg_at_1000
|
| 327 |
+
value: 51.39
|
| 328 |
+
- type: ndcg_at_3
|
| 329 |
+
value: 40.389
|
| 330 |
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- type: ndcg_at_5
|
| 331 |
+
value: 42.14
|
| 332 |
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- type: precision_at_1
|
| 333 |
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value: 36.051
|
| 334 |
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- type: precision_at_10
|
| 335 |
+
value: 8.446
|
| 336 |
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- type: precision_at_100
|
| 337 |
+
value: 1.411
|
| 338 |
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- type: precision_at_1000
|
| 339 |
+
value: 0.19
|
| 340 |
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- type: precision_at_3
|
| 341 |
+
value: 19.639
|
| 342 |
+
- type: precision_at_5
|
| 343 |
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value: 13.796
|
| 344 |
+
- type: recall_at_1
|
| 345 |
+
value: 28.538000000000004
|
| 346 |
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- type: recall_at_10
|
| 347 |
+
value: 54.99000000000001
|
| 348 |
+
- type: recall_at_100
|
| 349 |
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value: 75.098
|
| 350 |
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- type: recall_at_1000
|
| 351 |
+
value: 87.848
|
| 352 |
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- type: recall_at_3
|
| 353 |
+
value: 42.236000000000004
|
| 354 |
+
- type: recall_at_5
|
| 355 |
+
value: 47.377
|
| 356 |
+
- task:
|
| 357 |
+
type: Retrieval
|
| 358 |
+
dataset:
|
| 359 |
+
type: mteb/cqadupstack-gaming
|
| 360 |
+
name: MTEB CQADupstackGamingRetrieval
|
| 361 |
+
config: default
|
| 362 |
+
split: test
|
| 363 |
+
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
|
| 364 |
+
metrics:
|
| 365 |
+
- type: map_at_1
|
| 366 |
+
value: 37.188
|
| 367 |
+
- type: map_at_10
|
| 368 |
+
value: 50.861000000000004
|
| 369 |
+
- type: map_at_100
|
| 370 |
+
value: 51.917
|
| 371 |
+
- type: map_at_1000
|
| 372 |
+
value: 51.964999999999996
|
| 373 |
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- type: map_at_3
|
| 374 |
+
value: 47.144000000000005
|
| 375 |
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- type: map_at_5
|
| 376 |
+
value: 49.417
|
| 377 |
+
- type: mrr_at_1
|
| 378 |
+
value: 42.571
|
| 379 |
+
- type: mrr_at_10
|
| 380 |
+
value: 54.086999999999996
|
| 381 |
+
- type: mrr_at_100
|
| 382 |
+
value: 54.739000000000004
|
| 383 |
+
- type: mrr_at_1000
|
| 384 |
+
value: 54.762
|
| 385 |
+
- type: mrr_at_3
|
| 386 |
+
value: 51.285000000000004
|
| 387 |
+
- type: mrr_at_5
|
| 388 |
+
value: 53.0
|
| 389 |
+
- type: ndcg_at_1
|
| 390 |
+
value: 42.571
|
| 391 |
+
- type: ndcg_at_10
|
| 392 |
+
value: 57.282
|
| 393 |
+
- type: ndcg_at_100
|
| 394 |
+
value: 61.477000000000004
|
| 395 |
+
- type: ndcg_at_1000
|
| 396 |
+
value: 62.426
|
| 397 |
+
- type: ndcg_at_3
|
| 398 |
+
value: 51.0
|
| 399 |
+
- type: ndcg_at_5
|
| 400 |
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value: 54.346000000000004
|
| 401 |
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- type: precision_at_1
|
| 402 |
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value: 42.571
|
| 403 |
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- type: precision_at_10
|
| 404 |
+
value: 9.467
|
| 405 |
+
- type: precision_at_100
|
| 406 |
+
value: 1.2550000000000001
|
| 407 |
+
- type: precision_at_1000
|
| 408 |
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value: 0.13799999999999998
|
| 409 |
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- type: precision_at_3
|
| 410 |
+
value: 23.114
|
| 411 |
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- type: precision_at_5
|
| 412 |
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value: 16.250999999999998
|
| 413 |
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- type: recall_at_1
|
| 414 |
+
value: 37.188
|
| 415 |
+
- type: recall_at_10
|
| 416 |
+
value: 73.068
|
| 417 |
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- type: recall_at_100
|
| 418 |
+
value: 91.203
|
| 419 |
+
- type: recall_at_1000
|
| 420 |
+
value: 97.916
|
| 421 |
+
- type: recall_at_3
|
| 422 |
+
value: 56.552
|
| 423 |
+
- type: recall_at_5
|
| 424 |
+
value: 64.567
|
| 425 |
+
- task:
|
| 426 |
+
type: Retrieval
|
| 427 |
+
dataset:
|
| 428 |
+
type: mteb/cqadupstack-gis
|
| 429 |
+
name: MTEB CQADupstackGisRetrieval
|
| 430 |
+
config: default
|
| 431 |
+
split: test
|
| 432 |
+
revision: 5003b3064772da1887988e05400cf3806fe491f2
|
| 433 |
+
metrics:
|
| 434 |
+
- type: map_at_1
|
| 435 |
+
value: 25.041000000000004
|
| 436 |
+
- type: map_at_10
|
| 437 |
+
value: 33.86
|
| 438 |
+
- type: map_at_100
|
| 439 |
+
value: 34.988
|
| 440 |
+
- type: map_at_1000
|
| 441 |
+
value: 35.064
|
| 442 |
+
- type: map_at_3
|
| 443 |
+
value: 31.049
|
| 444 |
+
- type: map_at_5
|
| 445 |
+
value: 32.845
|
| 446 |
+
- type: mrr_at_1
|
| 447 |
+
value: 26.893
|
| 448 |
+
- type: mrr_at_10
|
| 449 |
+
value: 35.594
|
| 450 |
+
- type: mrr_at_100
|
| 451 |
+
value: 36.617
|
| 452 |
+
- type: mrr_at_1000
|
| 453 |
+
value: 36.671
|
| 454 |
+
- type: mrr_at_3
|
| 455 |
+
value: 33.051
|
| 456 |
+
- type: mrr_at_5
|
| 457 |
+
value: 34.61
|
| 458 |
+
- type: ndcg_at_1
|
| 459 |
+
value: 26.893
|
| 460 |
+
- type: ndcg_at_10
|
| 461 |
+
value: 38.674
|
| 462 |
+
- type: ndcg_at_100
|
| 463 |
+
value: 44.178
|
| 464 |
+
- type: ndcg_at_1000
|
| 465 |
+
value: 46.089999999999996
|
| 466 |
+
- type: ndcg_at_3
|
| 467 |
+
value: 33.485
|
| 468 |
+
- type: ndcg_at_5
|
| 469 |
+
value: 36.402
|
| 470 |
+
- type: precision_at_1
|
| 471 |
+
value: 26.893
|
| 472 |
+
- type: precision_at_10
|
| 473 |
+
value: 5.989
|
| 474 |
+
- type: precision_at_100
|
| 475 |
+
value: 0.918
|
| 476 |
+
- type: precision_at_1000
|
| 477 |
+
value: 0.11100000000000002
|
| 478 |
+
- type: precision_at_3
|
| 479 |
+
value: 14.2
|
| 480 |
+
- type: precision_at_5
|
| 481 |
+
value: 10.26
|
| 482 |
+
- type: recall_at_1
|
| 483 |
+
value: 25.041000000000004
|
| 484 |
+
- type: recall_at_10
|
| 485 |
+
value: 51.666000000000004
|
| 486 |
+
- type: recall_at_100
|
| 487 |
+
value: 76.896
|
| 488 |
+
- type: recall_at_1000
|
| 489 |
+
value: 91.243
|
| 490 |
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- type: recall_at_3
|
| 491 |
+
value: 38.035999999999994
|
| 492 |
+
- type: recall_at_5
|
| 493 |
+
value: 44.999
|
| 494 |
+
- task:
|
| 495 |
+
type: Retrieval
|
| 496 |
+
dataset:
|
| 497 |
+
type: mteb/cqadupstack-mathematica
|
| 498 |
+
name: MTEB CQADupstackMathematicaRetrieval
|
| 499 |
+
config: default
|
| 500 |
+
split: test
|
| 501 |
+
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
|
| 502 |
+
metrics:
|
| 503 |
+
- type: map_at_1
|
| 504 |
+
value: 15.909999999999998
|
| 505 |
+
- type: map_at_10
|
| 506 |
+
value: 23.901
|
| 507 |
+
- type: map_at_100
|
| 508 |
+
value: 25.165
|
| 509 |
+
- type: map_at_1000
|
| 510 |
+
value: 25.291000000000004
|
| 511 |
+
- type: map_at_3
|
| 512 |
+
value: 21.356
|
| 513 |
+
- type: map_at_5
|
| 514 |
+
value: 22.816
|
| 515 |
+
- type: mrr_at_1
|
| 516 |
+
value: 20.025000000000002
|
| 517 |
+
- type: mrr_at_10
|
| 518 |
+
value: 28.382
|
| 519 |
+
- type: mrr_at_100
|
| 520 |
+
value: 29.465000000000003
|
| 521 |
+
- type: mrr_at_1000
|
| 522 |
+
value: 29.535
|
| 523 |
+
- type: mrr_at_3
|
| 524 |
+
value: 25.933
|
| 525 |
+
- type: mrr_at_5
|
| 526 |
+
value: 27.332
|
| 527 |
+
- type: ndcg_at_1
|
| 528 |
+
value: 20.025000000000002
|
| 529 |
+
- type: ndcg_at_10
|
| 530 |
+
value: 29.099000000000004
|
| 531 |
+
- type: ndcg_at_100
|
| 532 |
+
value: 35.127
|
| 533 |
+
- type: ndcg_at_1000
|
| 534 |
+
value: 38.096000000000004
|
| 535 |
+
- type: ndcg_at_3
|
| 536 |
+
value: 24.464
|
| 537 |
+
- type: ndcg_at_5
|
| 538 |
+
value: 26.709
|
| 539 |
+
- type: precision_at_1
|
| 540 |
+
value: 20.025000000000002
|
| 541 |
+
- type: precision_at_10
|
| 542 |
+
value: 5.398
|
| 543 |
+
- type: precision_at_100
|
| 544 |
+
value: 0.9690000000000001
|
| 545 |
+
- type: precision_at_1000
|
| 546 |
+
value: 0.13699999999999998
|
| 547 |
+
- type: precision_at_3
|
| 548 |
+
value: 11.774
|
| 549 |
+
- type: precision_at_5
|
| 550 |
+
value: 8.632
|
| 551 |
+
- type: recall_at_1
|
| 552 |
+
value: 15.909999999999998
|
| 553 |
+
- type: recall_at_10
|
| 554 |
+
value: 40.672000000000004
|
| 555 |
+
- type: recall_at_100
|
| 556 |
+
value: 66.855
|
| 557 |
+
- type: recall_at_1000
|
| 558 |
+
value: 87.922
|
| 559 |
+
- type: recall_at_3
|
| 560 |
+
value: 28.069
|
| 561 |
+
- type: recall_at_5
|
| 562 |
+
value: 33.812
|
| 563 |
+
- task:
|
| 564 |
+
type: Retrieval
|
| 565 |
+
dataset:
|
| 566 |
+
type: mteb/cqadupstack-physics
|
| 567 |
+
name: MTEB CQADupstackPhysicsRetrieval
|
| 568 |
+
config: default
|
| 569 |
+
split: test
|
| 570 |
+
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
|
| 571 |
+
metrics:
|
| 572 |
+
- type: map_at_1
|
| 573 |
+
value: 30.175
|
| 574 |
+
- type: map_at_10
|
| 575 |
+
value: 41.36
|
| 576 |
+
- type: map_at_100
|
| 577 |
+
value: 42.701
|
| 578 |
+
- type: map_at_1000
|
| 579 |
+
value: 42.817
|
| 580 |
+
- type: map_at_3
|
| 581 |
+
value: 37.931
|
| 582 |
+
- type: map_at_5
|
| 583 |
+
value: 39.943
|
| 584 |
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- type: mrr_at_1
|
| 585 |
+
value: 35.611
|
| 586 |
+
- type: mrr_at_10
|
| 587 |
+
value: 46.346
|
| 588 |
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- type: mrr_at_100
|
| 589 |
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value: 47.160000000000004
|
| 590 |
+
- type: mrr_at_1000
|
| 591 |
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value: 47.203
|
| 592 |
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- type: mrr_at_3
|
| 593 |
+
value: 43.712
|
| 594 |
+
- type: mrr_at_5
|
| 595 |
+
value: 45.367000000000004
|
| 596 |
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- type: ndcg_at_1
|
| 597 |
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value: 35.611
|
| 598 |
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- type: ndcg_at_10
|
| 599 |
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value: 47.532000000000004
|
| 600 |
+
- type: ndcg_at_100
|
| 601 |
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value: 53.003
|
| 602 |
+
- type: ndcg_at_1000
|
| 603 |
+
value: 55.007
|
| 604 |
+
- type: ndcg_at_3
|
| 605 |
+
value: 42.043
|
| 606 |
+
- type: ndcg_at_5
|
| 607 |
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value: 44.86
|
| 608 |
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- type: precision_at_1
|
| 609 |
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value: 35.611
|
| 610 |
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- type: precision_at_10
|
| 611 |
+
value: 8.624
|
| 612 |
+
- type: precision_at_100
|
| 613 |
+
value: 1.332
|
| 614 |
+
- type: precision_at_1000
|
| 615 |
+
value: 0.169
|
| 616 |
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- type: precision_at_3
|
| 617 |
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value: 20.083000000000002
|
| 618 |
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- type: precision_at_5
|
| 619 |
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value: 14.437
|
| 620 |
+
- type: recall_at_1
|
| 621 |
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value: 30.175
|
| 622 |
+
- type: recall_at_10
|
| 623 |
+
value: 60.5
|
| 624 |
+
- type: recall_at_100
|
| 625 |
+
value: 83.399
|
| 626 |
+
- type: recall_at_1000
|
| 627 |
+
value: 96.255
|
| 628 |
+
- type: recall_at_3
|
| 629 |
+
value: 45.448
|
| 630 |
+
- type: recall_at_5
|
| 631 |
+
value: 52.432
|
| 632 |
+
- task:
|
| 633 |
+
type: Retrieval
|
| 634 |
+
dataset:
|
| 635 |
+
type: mteb/cqadupstack-programmers
|
| 636 |
+
name: MTEB CQADupstackProgrammersRetrieval
|
| 637 |
+
config: default
|
| 638 |
+
split: test
|
| 639 |
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revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
|
| 640 |
+
metrics:
|
| 641 |
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- type: map_at_1
|
| 642 |
+
value: 22.467000000000002
|
| 643 |
+
- type: map_at_10
|
| 644 |
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value: 33.812999999999995
|
| 645 |
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- type: map_at_100
|
| 646 |
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value: 35.248000000000005
|
| 647 |
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- type: map_at_1000
|
| 648 |
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value: 35.359
|
| 649 |
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- type: map_at_3
|
| 650 |
+
value: 30.316
|
| 651 |
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- type: map_at_5
|
| 652 |
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value: 32.233000000000004
|
| 653 |
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- type: mrr_at_1
|
| 654 |
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value: 28.310999999999996
|
| 655 |
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- type: mrr_at_10
|
| 656 |
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value: 38.979
|
| 657 |
+
- type: mrr_at_100
|
| 658 |
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value: 39.937
|
| 659 |
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- type: mrr_at_1000
|
| 660 |
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value: 39.989999999999995
|
| 661 |
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- type: mrr_at_3
|
| 662 |
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value: 36.244
|
| 663 |
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- type: mrr_at_5
|
| 664 |
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value: 37.871
|
| 665 |
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- type: ndcg_at_1
|
| 666 |
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value: 28.310999999999996
|
| 667 |
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- type: ndcg_at_10
|
| 668 |
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value: 40.282000000000004
|
| 669 |
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- type: ndcg_at_100
|
| 670 |
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value: 46.22
|
| 671 |
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- type: ndcg_at_1000
|
| 672 |
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value: 48.507
|
| 673 |
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- type: ndcg_at_3
|
| 674 |
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value: 34.596
|
| 675 |
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|
| 676 |
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value: 37.267
|
| 677 |
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- type: precision_at_1
|
| 678 |
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value: 28.310999999999996
|
| 679 |
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|
| 680 |
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value: 7.831
|
| 681 |
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|
| 682 |
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value: 1.257
|
| 683 |
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|
| 684 |
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value: 0.164
|
| 685 |
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|
| 686 |
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value: 17.275
|
| 687 |
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|
| 688 |
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value: 12.556999999999999
|
| 689 |
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- type: recall_at_1
|
| 690 |
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value: 22.467000000000002
|
| 691 |
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- type: recall_at_10
|
| 692 |
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value: 54.14099999999999
|
| 693 |
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- type: recall_at_100
|
| 694 |
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value: 79.593
|
| 695 |
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- type: recall_at_1000
|
| 696 |
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value: 95.063
|
| 697 |
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- type: recall_at_3
|
| 698 |
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value: 38.539
|
| 699 |
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- type: recall_at_5
|
| 700 |
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value: 45.403
|
| 701 |
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- task:
|
| 702 |
+
type: Retrieval
|
| 703 |
+
dataset:
|
| 704 |
+
type: mteb/cqadupstack
|
| 705 |
+
name: MTEB CQADupstackRetrieval
|
| 706 |
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config: default
|
| 707 |
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split: test
|
| 708 |
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revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
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| 709 |
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metrics:
|
| 710 |
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- type: map_at_1
|
| 711 |
+
value: 24.18591666666667
|
| 712 |
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- type: map_at_10
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| 713 |
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value: 33.84258333333333
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| 714 |
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| 715 |
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value: 35.11391666666666
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| 716 |
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| 717 |
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value: 35.23258333333333
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| 718 |
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| 719 |
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value: 30.764249999999997
|
| 720 |
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|
| 721 |
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value: 32.52333333333334
|
| 722 |
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|
| 723 |
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value: 28.54733333333333
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| 724 |
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|
| 725 |
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value: 37.81725
|
| 726 |
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|
| 727 |
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value: 38.716499999999996
|
| 728 |
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|
| 729 |
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value: 38.77458333333333
|
| 730 |
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|
| 731 |
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value: 35.157833333333336
|
| 732 |
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|
| 733 |
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value: 36.69816666666667
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| 734 |
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|
| 735 |
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value: 28.54733333333333
|
| 736 |
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|
| 737 |
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value: 39.51508333333334
|
| 738 |
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- type: ndcg_at_100
|
| 739 |
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value: 44.95316666666666
|
| 740 |
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|
| 741 |
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value: 47.257083333333334
|
| 742 |
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|
| 743 |
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value: 34.205833333333324
|
| 744 |
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|
| 745 |
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value: 36.78266666666667
|
| 746 |
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|
| 747 |
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value: 28.54733333333333
|
| 748 |
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|
| 749 |
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value: 7.082583333333334
|
| 750 |
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|
| 751 |
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value: 1.1590833333333332
|
| 752 |
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- type: precision_at_1000
|
| 753 |
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value: 0.15516666666666662
|
| 754 |
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- type: precision_at_3
|
| 755 |
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value: 15.908750000000001
|
| 756 |
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- type: precision_at_5
|
| 757 |
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value: 11.505416666666669
|
| 758 |
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|
| 759 |
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value: 24.18591666666667
|
| 760 |
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- type: recall_at_10
|
| 761 |
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value: 52.38758333333333
|
| 762 |
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- type: recall_at_100
|
| 763 |
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value: 76.13666666666667
|
| 764 |
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- type: recall_at_1000
|
| 765 |
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value: 91.99066666666667
|
| 766 |
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- type: recall_at_3
|
| 767 |
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value: 37.78333333333334
|
| 768 |
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- type: recall_at_5
|
| 769 |
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value: 44.30141666666666
|
| 770 |
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- task:
|
| 771 |
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type: Retrieval
|
| 772 |
+
dataset:
|
| 773 |
+
type: mteb/cqadupstack-stats
|
| 774 |
+
name: MTEB CQADupstackStatsRetrieval
|
| 775 |
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config: default
|
| 776 |
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split: test
|
| 777 |
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revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
|
| 778 |
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metrics:
|
| 779 |
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|
| 780 |
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value: 21.975
|
| 781 |
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- type: map_at_10
|
| 782 |
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value: 29.781000000000002
|
| 783 |
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- type: map_at_100
|
| 784 |
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value: 30.847
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| 785 |
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|
| 786 |
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value: 30.94
|
| 787 |
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|
| 788 |
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value: 27.167
|
| 789 |
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|
| 790 |
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value: 28.633999999999997
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| 791 |
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- type: mrr_at_1
|
| 792 |
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value: 24.387
|
| 793 |
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- type: mrr_at_10
|
| 794 |
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value: 32.476
|
| 795 |
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|
| 796 |
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value: 33.337
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| 797 |
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|
| 798 |
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value: 33.403
|
| 799 |
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- type: mrr_at_3
|
| 800 |
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value: 29.881999999999998
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| 801 |
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- type: mrr_at_5
|
| 802 |
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value: 31.339
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| 803 |
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- type: ndcg_at_1
|
| 804 |
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value: 24.387
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| 805 |
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|
| 806 |
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value: 34.596
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| 807 |
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|
| 808 |
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value: 39.635
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| 809 |
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- type: ndcg_at_1000
|
| 810 |
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value: 42.079
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| 811 |
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|
| 812 |
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value: 29.516
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| 813 |
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| 814 |
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value: 31.959
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| 815 |
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| 816 |
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value: 24.387
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| 817 |
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|
| 818 |
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value: 5.6129999999999995
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| 819 |
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| 820 |
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value: 0.8909999999999999
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| 821 |
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|
| 822 |
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value: 0.117
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| 823 |
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|
| 824 |
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value: 12.73
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| 825 |
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|
| 826 |
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value: 9.171999999999999
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| 827 |
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- type: recall_at_1
|
| 828 |
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value: 21.975
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| 829 |
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- type: recall_at_10
|
| 830 |
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value: 46.826
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| 831 |
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- type: recall_at_100
|
| 832 |
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value: 69.554
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| 833 |
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- type: recall_at_1000
|
| 834 |
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value: 87.749
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| 835 |
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- type: recall_at_3
|
| 836 |
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value: 33.016
|
| 837 |
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- type: recall_at_5
|
| 838 |
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value: 38.97
|
| 839 |
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- task:
|
| 840 |
+
type: Retrieval
|
| 841 |
+
dataset:
|
| 842 |
+
type: mteb/cqadupstack-tex
|
| 843 |
+
name: MTEB CQADupstackTexRetrieval
|
| 844 |
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config: default
|
| 845 |
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split: test
|
| 846 |
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revision: 46989137a86843e03a6195de44b09deda022eec7
|
| 847 |
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metrics:
|
| 848 |
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|
| 849 |
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value: 15.614
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| 850 |
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|
| 851 |
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value: 22.927
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| 852 |
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|
| 853 |
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value: 24.185000000000002
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| 854 |
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- type: map_at_1000
|
| 855 |
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value: 24.319
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| 856 |
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|
| 857 |
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value: 20.596
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| 858 |
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|
| 859 |
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value: 21.854000000000003
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| 860 |
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|
| 861 |
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value: 18.858
|
| 862 |
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|
| 863 |
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value: 26.535999999999998
|
| 864 |
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|
| 865 |
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value: 27.582
|
| 866 |
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|
| 867 |
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value: 27.665
|
| 868 |
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|
| 869 |
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value: 24.295
|
| 870 |
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|
| 871 |
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value: 25.532
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| 872 |
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|
| 873 |
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value: 18.858
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| 874 |
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|
| 875 |
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value: 27.583000000000002
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| 876 |
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|
| 877 |
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value: 33.635
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| 878 |
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|
| 879 |
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value: 36.647
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| 880 |
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|
| 881 |
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value: 23.348
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| 882 |
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|
| 883 |
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value: 25.257
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| 884 |
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|
| 885 |
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value: 18.858
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| 886 |
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|
| 887 |
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value: 5.158
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| 888 |
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|
| 889 |
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value: 0.964
|
| 890 |
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|
| 891 |
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value: 0.13999999999999999
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| 892 |
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|
| 893 |
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value: 11.092
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| 894 |
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|
| 895 |
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value: 8.1
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| 896 |
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- type: recall_at_1
|
| 897 |
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value: 15.614
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| 898 |
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- type: recall_at_10
|
| 899 |
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value: 37.916
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| 900 |
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- type: recall_at_100
|
| 901 |
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value: 65.205
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| 902 |
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|
| 903 |
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value: 86.453
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| 904 |
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- type: recall_at_3
|
| 905 |
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value: 26.137
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| 906 |
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- type: recall_at_5
|
| 907 |
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value: 31.087999999999997
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| 908 |
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- task:
|
| 909 |
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type: Retrieval
|
| 910 |
+
dataset:
|
| 911 |
+
type: mteb/cqadupstack-unix
|
| 912 |
+
name: MTEB CQADupstackUnixRetrieval
|
| 913 |
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config: default
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| 914 |
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split: test
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| 915 |
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revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
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| 916 |
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metrics:
|
| 917 |
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|
| 918 |
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value: 23.078000000000003
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| 919 |
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- type: map_at_10
|
| 920 |
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value: 31.941999999999997
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| 921 |
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|
| 922 |
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value: 33.196999999999996
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| 923 |
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| 924 |
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value: 33.303
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| 925 |
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|
| 926 |
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value: 28.927000000000003
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| 927 |
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|
| 928 |
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value: 30.707
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|
| 930 |
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value: 26.866
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| 931 |
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|
| 932 |
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value: 35.557
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| 933 |
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|
| 934 |
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value: 36.569
|
| 935 |
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|
| 936 |
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value: 36.632
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| 937 |
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|
| 938 |
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value: 32.897999999999996
|
| 939 |
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|
| 940 |
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value: 34.437
|
| 941 |
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|
| 942 |
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value: 26.866
|
| 943 |
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|
| 944 |
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value: 37.372
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| 945 |
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|
| 946 |
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value: 43.248
|
| 947 |
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|
| 948 |
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value: 45.632
|
| 949 |
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|
| 950 |
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value: 31.852999999999998
|
| 951 |
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- type: ndcg_at_5
|
| 952 |
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value: 34.582
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| 953 |
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|
| 954 |
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value: 26.866
|
| 955 |
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- type: precision_at_10
|
| 956 |
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value: 6.511
|
| 957 |
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|
| 958 |
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value: 1.078
|
| 959 |
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|
| 960 |
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value: 0.13899999999999998
|
| 961 |
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|
| 962 |
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value: 14.582999999999998
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| 963 |
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|
| 964 |
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value: 10.634
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| 965 |
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|
| 966 |
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value: 23.078000000000003
|
| 967 |
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- type: recall_at_10
|
| 968 |
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value: 50.334
|
| 969 |
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|
| 970 |
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value: 75.787
|
| 971 |
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- type: recall_at_1000
|
| 972 |
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value: 92.485
|
| 973 |
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- type: recall_at_3
|
| 974 |
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value: 35.386
|
| 975 |
+
- type: recall_at_5
|
| 976 |
+
value: 42.225
|
| 977 |
+
- task:
|
| 978 |
+
type: Retrieval
|
| 979 |
+
dataset:
|
| 980 |
+
type: mteb/cqadupstack-webmasters
|
| 981 |
+
name: MTEB CQADupstackWebmastersRetrieval
|
| 982 |
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config: default
|
| 983 |
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split: test
|
| 984 |
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revision: 160c094312a0e1facb97e55eeddb698c0abe3571
|
| 985 |
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metrics:
|
| 986 |
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|
| 987 |
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value: 22.203999999999997
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| 988 |
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|
| 989 |
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value: 31.276
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| 990 |
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|
| 991 |
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value: 32.844
|
| 992 |
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|
| 993 |
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value: 33.062999999999995
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| 994 |
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| 995 |
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value: 27.733999999999998
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|
| 997 |
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value: 29.64
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| 998 |
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|
| 999 |
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value: 27.272999999999996
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| 1000 |
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|
| 1001 |
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value: 36.083
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| 1002 |
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|
| 1003 |
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value: 37.008
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| 1004 |
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|
| 1005 |
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value: 37.076
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| 1006 |
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|
| 1007 |
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value: 33.004
|
| 1008 |
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|
| 1009 |
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value: 34.664
|
| 1010 |
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|
| 1011 |
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value: 27.272999999999996
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|
| 1013 |
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value: 37.763000000000005
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|
| 1015 |
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value: 43.566
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| 1016 |
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|
| 1017 |
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value: 46.356
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| 1018 |
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|
| 1019 |
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value: 31.673000000000002
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| 1020 |
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| 1021 |
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value: 34.501
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| 1022 |
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- type: precision_at_1
|
| 1023 |
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value: 27.272999999999996
|
| 1024 |
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- type: precision_at_10
|
| 1025 |
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value: 7.470000000000001
|
| 1026 |
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- type: precision_at_100
|
| 1027 |
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value: 1.502
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| 1028 |
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|
| 1029 |
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value: 0.24
|
| 1030 |
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|
| 1031 |
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value: 14.756
|
| 1032 |
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- type: precision_at_5
|
| 1033 |
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value: 11.225
|
| 1034 |
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- type: recall_at_1
|
| 1035 |
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value: 22.203999999999997
|
| 1036 |
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- type: recall_at_10
|
| 1037 |
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value: 51.437999999999995
|
| 1038 |
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- type: recall_at_100
|
| 1039 |
+
value: 76.845
|
| 1040 |
+
- type: recall_at_1000
|
| 1041 |
+
value: 94.38600000000001
|
| 1042 |
+
- type: recall_at_3
|
| 1043 |
+
value: 34.258
|
| 1044 |
+
- type: recall_at_5
|
| 1045 |
+
value: 41.512
|
| 1046 |
+
- task:
|
| 1047 |
+
type: Retrieval
|
| 1048 |
+
dataset:
|
| 1049 |
+
type: mteb/cqadupstack-wordpress
|
| 1050 |
+
name: MTEB CQADupstackWordpressRetrieval
|
| 1051 |
+
config: default
|
| 1052 |
+
split: test
|
| 1053 |
+
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
|
| 1054 |
+
metrics:
|
| 1055 |
+
- type: map_at_1
|
| 1056 |
+
value: 17.474
|
| 1057 |
+
- type: map_at_10
|
| 1058 |
+
value: 26.362999999999996
|
| 1059 |
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- type: map_at_100
|
| 1060 |
+
value: 27.456999999999997
|
| 1061 |
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- type: map_at_1000
|
| 1062 |
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value: 27.567999999999998
|
| 1063 |
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- type: map_at_3
|
| 1064 |
+
value: 23.518
|
| 1065 |
+
- type: map_at_5
|
| 1066 |
+
value: 25.068
|
| 1067 |
+
- type: mrr_at_1
|
| 1068 |
+
value: 18.669
|
| 1069 |
+
- type: mrr_at_10
|
| 1070 |
+
value: 27.998
|
| 1071 |
+
- type: mrr_at_100
|
| 1072 |
+
value: 28.953
|
| 1073 |
+
- type: mrr_at_1000
|
| 1074 |
+
value: 29.03
|
| 1075 |
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- type: mrr_at_3
|
| 1076 |
+
value: 25.230999999999998
|
| 1077 |
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- type: mrr_at_5
|
| 1078 |
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value: 26.654
|
| 1079 |
+
- type: ndcg_at_1
|
| 1080 |
+
value: 18.669
|
| 1081 |
+
- type: ndcg_at_10
|
| 1082 |
+
value: 31.684
|
| 1083 |
+
- type: ndcg_at_100
|
| 1084 |
+
value: 36.864999999999995
|
| 1085 |
+
- type: ndcg_at_1000
|
| 1086 |
+
value: 39.555
|
| 1087 |
+
- type: ndcg_at_3
|
| 1088 |
+
value: 26.057000000000002
|
| 1089 |
+
- type: ndcg_at_5
|
| 1090 |
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value: 28.587
|
| 1091 |
+
- type: precision_at_1
|
| 1092 |
+
value: 18.669
|
| 1093 |
+
- type: precision_at_10
|
| 1094 |
+
value: 5.3420000000000005
|
| 1095 |
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- type: precision_at_100
|
| 1096 |
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value: 0.847
|
| 1097 |
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- type: precision_at_1000
|
| 1098 |
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value: 0.12
|
| 1099 |
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- type: precision_at_3
|
| 1100 |
+
value: 11.583
|
| 1101 |
+
- type: precision_at_5
|
| 1102 |
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value: 8.466
|
| 1103 |
+
- type: recall_at_1
|
| 1104 |
+
value: 17.474
|
| 1105 |
+
- type: recall_at_10
|
| 1106 |
+
value: 46.497
|
| 1107 |
+
- type: recall_at_100
|
| 1108 |
+
value: 69.977
|
| 1109 |
+
- type: recall_at_1000
|
| 1110 |
+
value: 89.872
|
| 1111 |
+
- type: recall_at_3
|
| 1112 |
+
value: 31.385999999999996
|
| 1113 |
+
- type: recall_at_5
|
| 1114 |
+
value: 37.283
|
| 1115 |
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- task:
|
| 1116 |
+
type: Retrieval
|
| 1117 |
+
dataset:
|
| 1118 |
+
type: mteb/climate-fever
|
| 1119 |
+
name: MTEB ClimateFEVER
|
| 1120 |
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config: default
|
| 1121 |
+
split: test
|
| 1122 |
+
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
|
| 1123 |
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metrics:
|
| 1124 |
+
- type: map_at_1
|
| 1125 |
+
value: 17.173
|
| 1126 |
+
- type: map_at_10
|
| 1127 |
+
value: 30.407
|
| 1128 |
+
- type: map_at_100
|
| 1129 |
+
value: 32.528
|
| 1130 |
+
- type: map_at_1000
|
| 1131 |
+
value: 32.698
|
| 1132 |
+
- type: map_at_3
|
| 1133 |
+
value: 25.523
|
| 1134 |
+
- type: map_at_5
|
| 1135 |
+
value: 28.038
|
| 1136 |
+
- type: mrr_at_1
|
| 1137 |
+
value: 38.958
|
| 1138 |
+
- type: mrr_at_10
|
| 1139 |
+
value: 51.515
|
| 1140 |
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- type: mrr_at_100
|
| 1141 |
+
value: 52.214000000000006
|
| 1142 |
+
- type: mrr_at_1000
|
| 1143 |
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value: 52.237
|
| 1144 |
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- type: mrr_at_3
|
| 1145 |
+
value: 48.502
|
| 1146 |
+
- type: mrr_at_5
|
| 1147 |
+
value: 50.251000000000005
|
| 1148 |
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- type: ndcg_at_1
|
| 1149 |
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value: 38.958
|
| 1150 |
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- type: ndcg_at_10
|
| 1151 |
+
value: 40.355000000000004
|
| 1152 |
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- type: ndcg_at_100
|
| 1153 |
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value: 47.68
|
| 1154 |
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- type: ndcg_at_1000
|
| 1155 |
+
value: 50.370000000000005
|
| 1156 |
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- type: ndcg_at_3
|
| 1157 |
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value: 33.946
|
| 1158 |
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- type: ndcg_at_5
|
| 1159 |
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value: 36.057
|
| 1160 |
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- type: precision_at_1
|
| 1161 |
+
value: 38.958
|
| 1162 |
+
- type: precision_at_10
|
| 1163 |
+
value: 12.508
|
| 1164 |
+
- type: precision_at_100
|
| 1165 |
+
value: 2.054
|
| 1166 |
+
- type: precision_at_1000
|
| 1167 |
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value: 0.256
|
| 1168 |
+
- type: precision_at_3
|
| 1169 |
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value: 25.581
|
| 1170 |
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- type: precision_at_5
|
| 1171 |
+
value: 19.256999999999998
|
| 1172 |
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- type: recall_at_1
|
| 1173 |
+
value: 17.173
|
| 1174 |
+
- type: recall_at_10
|
| 1175 |
+
value: 46.967
|
| 1176 |
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- type: recall_at_100
|
| 1177 |
+
value: 71.47200000000001
|
| 1178 |
+
- type: recall_at_1000
|
| 1179 |
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value: 86.238
|
| 1180 |
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- type: recall_at_3
|
| 1181 |
+
value: 30.961
|
| 1182 |
+
- type: recall_at_5
|
| 1183 |
+
value: 37.539
|
| 1184 |
+
- task:
|
| 1185 |
+
type: Retrieval
|
| 1186 |
+
dataset:
|
| 1187 |
+
type: mteb/dbpedia
|
| 1188 |
+
name: MTEB DBPedia
|
| 1189 |
+
config: default
|
| 1190 |
+
split: test
|
| 1191 |
+
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
|
| 1192 |
+
metrics:
|
| 1193 |
+
- type: map_at_1
|
| 1194 |
+
value: 8.999
|
| 1195 |
+
- type: map_at_10
|
| 1196 |
+
value: 18.989
|
| 1197 |
+
- type: map_at_100
|
| 1198 |
+
value: 26.133
|
| 1199 |
+
- type: map_at_1000
|
| 1200 |
+
value: 27.666
|
| 1201 |
+
- type: map_at_3
|
| 1202 |
+
value: 13.918
|
| 1203 |
+
- type: map_at_5
|
| 1204 |
+
value: 16.473
|
| 1205 |
+
- type: mrr_at_1
|
| 1206 |
+
value: 66.25
|
| 1207 |
+
- type: mrr_at_10
|
| 1208 |
+
value: 74.161
|
| 1209 |
+
- type: mrr_at_100
|
| 1210 |
+
value: 74.516
|
| 1211 |
+
- type: mrr_at_1000
|
| 1212 |
+
value: 74.524
|
| 1213 |
+
- type: mrr_at_3
|
| 1214 |
+
value: 72.875
|
| 1215 |
+
- type: mrr_at_5
|
| 1216 |
+
value: 73.613
|
| 1217 |
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- type: ndcg_at_1
|
| 1218 |
+
value: 54.37499999999999
|
| 1219 |
+
- type: ndcg_at_10
|
| 1220 |
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value: 39.902
|
| 1221 |
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- type: ndcg_at_100
|
| 1222 |
+
value: 44.212
|
| 1223 |
+
- type: ndcg_at_1000
|
| 1224 |
+
value: 51.62
|
| 1225 |
+
- type: ndcg_at_3
|
| 1226 |
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value: 45.193
|
| 1227 |
+
- type: ndcg_at_5
|
| 1228 |
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value: 42.541000000000004
|
| 1229 |
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- type: precision_at_1
|
| 1230 |
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value: 66.25
|
| 1231 |
+
- type: precision_at_10
|
| 1232 |
+
value: 30.425
|
| 1233 |
+
- type: precision_at_100
|
| 1234 |
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value: 9.754999999999999
|
| 1235 |
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- type: precision_at_1000
|
| 1236 |
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value: 2.043
|
| 1237 |
+
- type: precision_at_3
|
| 1238 |
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value: 48.25
|
| 1239 |
+
- type: precision_at_5
|
| 1240 |
+
value: 40.65
|
| 1241 |
+
- type: recall_at_1
|
| 1242 |
+
value: 8.999
|
| 1243 |
+
- type: recall_at_10
|
| 1244 |
+
value: 24.133
|
| 1245 |
+
- type: recall_at_100
|
| 1246 |
+
value: 49.138999999999996
|
| 1247 |
+
- type: recall_at_1000
|
| 1248 |
+
value: 72.639
|
| 1249 |
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- type: recall_at_3
|
| 1250 |
+
value: 15.287999999999998
|
| 1251 |
+
- type: recall_at_5
|
| 1252 |
+
value: 19.415
|
| 1253 |
+
- task:
|
| 1254 |
+
type: Classification
|
| 1255 |
+
dataset:
|
| 1256 |
+
type: mteb/emotion
|
| 1257 |
+
name: MTEB EmotionClassification
|
| 1258 |
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config: default
|
| 1259 |
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split: test
|
| 1260 |
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revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
|
| 1261 |
+
metrics:
|
| 1262 |
+
- type: accuracy
|
| 1263 |
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value: 46.38999999999999
|
| 1264 |
+
- type: f1
|
| 1265 |
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value: 41.444205512055234
|
| 1266 |
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- task:
|
| 1267 |
+
type: Retrieval
|
| 1268 |
+
dataset:
|
| 1269 |
+
type: mteb/fever
|
| 1270 |
+
name: MTEB FEVER
|
| 1271 |
+
config: default
|
| 1272 |
+
split: test
|
| 1273 |
+
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
|
| 1274 |
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metrics:
|
| 1275 |
+
- type: map_at_1
|
| 1276 |
+
value: 87.35000000000001
|
| 1277 |
+
- type: map_at_10
|
| 1278 |
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value: 92.837
|
| 1279 |
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- type: map_at_100
|
| 1280 |
+
value: 92.996
|
| 1281 |
+
- type: map_at_1000
|
| 1282 |
+
value: 93.006
|
| 1283 |
+
- type: map_at_3
|
| 1284 |
+
value: 92.187
|
| 1285 |
+
- type: map_at_5
|
| 1286 |
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value: 92.595
|
| 1287 |
+
- type: mrr_at_1
|
| 1288 |
+
value: 93.864
|
| 1289 |
+
- type: mrr_at_10
|
| 1290 |
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value: 96.723
|
| 1291 |
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- type: mrr_at_100
|
| 1292 |
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value: 96.72500000000001
|
| 1293 |
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- type: mrr_at_1000
|
| 1294 |
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value: 96.72500000000001
|
| 1295 |
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- type: mrr_at_3
|
| 1296 |
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value: 96.64
|
| 1297 |
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- type: mrr_at_5
|
| 1298 |
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value: 96.71499999999999
|
| 1299 |
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- type: ndcg_at_1
|
| 1300 |
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value: 93.864
|
| 1301 |
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- type: ndcg_at_10
|
| 1302 |
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value: 94.813
|
| 1303 |
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- type: ndcg_at_100
|
| 1304 |
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value: 95.243
|
| 1305 |
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- type: ndcg_at_1000
|
| 1306 |
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value: 95.38600000000001
|
| 1307 |
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- type: ndcg_at_3
|
| 1308 |
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value: 94.196
|
| 1309 |
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- type: ndcg_at_5
|
| 1310 |
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value: 94.521
|
| 1311 |
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- type: precision_at_1
|
| 1312 |
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value: 93.864
|
| 1313 |
+
- type: precision_at_10
|
| 1314 |
+
value: 10.951
|
| 1315 |
+
- type: precision_at_100
|
| 1316 |
+
value: 1.1400000000000001
|
| 1317 |
+
- type: precision_at_1000
|
| 1318 |
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value: 0.117
|
| 1319 |
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- type: precision_at_3
|
| 1320 |
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value: 35.114000000000004
|
| 1321 |
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- type: precision_at_5
|
| 1322 |
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value: 21.476
|
| 1323 |
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- type: recall_at_1
|
| 1324 |
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value: 87.35000000000001
|
| 1325 |
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- type: recall_at_10
|
| 1326 |
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value: 96.941
|
| 1327 |
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- type: recall_at_100
|
| 1328 |
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value: 98.397
|
| 1329 |
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- type: recall_at_1000
|
| 1330 |
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value: 99.21600000000001
|
| 1331 |
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- type: recall_at_3
|
| 1332 |
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value: 95.149
|
| 1333 |
+
- type: recall_at_5
|
| 1334 |
+
value: 96.131
|
| 1335 |
+
- task:
|
| 1336 |
+
type: Retrieval
|
| 1337 |
+
dataset:
|
| 1338 |
+
type: mteb/fiqa
|
| 1339 |
+
name: MTEB FiQA2018
|
| 1340 |
+
config: default
|
| 1341 |
+
split: test
|
| 1342 |
+
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
|
| 1343 |
+
metrics:
|
| 1344 |
+
- type: map_at_1
|
| 1345 |
+
value: 24.476
|
| 1346 |
+
- type: map_at_10
|
| 1347 |
+
value: 40.11
|
| 1348 |
+
- type: map_at_100
|
| 1349 |
+
value: 42.229
|
| 1350 |
+
- type: map_at_1000
|
| 1351 |
+
value: 42.378
|
| 1352 |
+
- type: map_at_3
|
| 1353 |
+
value: 34.512
|
| 1354 |
+
- type: map_at_5
|
| 1355 |
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value: 38.037
|
| 1356 |
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- type: mrr_at_1
|
| 1357 |
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value: 47.839999999999996
|
| 1358 |
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- type: mrr_at_10
|
| 1359 |
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value: 57.053
|
| 1360 |
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- type: mrr_at_100
|
| 1361 |
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value: 57.772
|
| 1362 |
+
- type: mrr_at_1000
|
| 1363 |
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value: 57.799
|
| 1364 |
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- type: mrr_at_3
|
| 1365 |
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value: 54.552
|
| 1366 |
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- type: mrr_at_5
|
| 1367 |
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value: 56.011
|
| 1368 |
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- type: ndcg_at_1
|
| 1369 |
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value: 47.839999999999996
|
| 1370 |
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- type: ndcg_at_10
|
| 1371 |
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value: 48.650999999999996
|
| 1372 |
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- type: ndcg_at_100
|
| 1373 |
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value: 55.681000000000004
|
| 1374 |
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- type: ndcg_at_1000
|
| 1375 |
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value: 57.979
|
| 1376 |
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- type: ndcg_at_3
|
| 1377 |
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value: 43.923
|
| 1378 |
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- type: ndcg_at_5
|
| 1379 |
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value: 46.037
|
| 1380 |
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- type: precision_at_1
|
| 1381 |
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value: 47.839999999999996
|
| 1382 |
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- type: precision_at_10
|
| 1383 |
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value: 13.395000000000001
|
| 1384 |
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- type: precision_at_100
|
| 1385 |
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value: 2.0660000000000003
|
| 1386 |
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- type: precision_at_1000
|
| 1387 |
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value: 0.248
|
| 1388 |
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- type: precision_at_3
|
| 1389 |
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value: 29.064
|
| 1390 |
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- type: precision_at_5
|
| 1391 |
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value: 22.006
|
| 1392 |
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- type: recall_at_1
|
| 1393 |
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value: 24.476
|
| 1394 |
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- type: recall_at_10
|
| 1395 |
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value: 56.216
|
| 1396 |
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- type: recall_at_100
|
| 1397 |
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value: 81.798
|
| 1398 |
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- type: recall_at_1000
|
| 1399 |
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value: 95.48299999999999
|
| 1400 |
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- type: recall_at_3
|
| 1401 |
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value: 39.357
|
| 1402 |
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- type: recall_at_5
|
| 1403 |
+
value: 47.802
|
| 1404 |
+
- task:
|
| 1405 |
+
type: Retrieval
|
| 1406 |
+
dataset:
|
| 1407 |
+
type: mteb/hotpotqa
|
| 1408 |
+
name: MTEB HotpotQA
|
| 1409 |
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config: default
|
| 1410 |
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split: test
|
| 1411 |
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revision: ab518f4d6fcca38d87c25209f94beba119d02014
|
| 1412 |
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metrics:
|
| 1413 |
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- type: map_at_1
|
| 1414 |
+
value: 42.728
|
| 1415 |
+
- type: map_at_10
|
| 1416 |
+
value: 57.737
|
| 1417 |
+
- type: map_at_100
|
| 1418 |
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value: 58.531
|
| 1419 |
+
- type: map_at_1000
|
| 1420 |
+
value: 58.594
|
| 1421 |
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- type: map_at_3
|
| 1422 |
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value: 54.869
|
| 1423 |
+
- type: map_at_5
|
| 1424 |
+
value: 56.55
|
| 1425 |
+
- type: mrr_at_1
|
| 1426 |
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value: 85.456
|
| 1427 |
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- type: mrr_at_10
|
| 1428 |
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value: 90.062
|
| 1429 |
+
- type: mrr_at_100
|
| 1430 |
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value: 90.159
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| 1431 |
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- type: mrr_at_1000
|
| 1432 |
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value: 90.16
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| 1433 |
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- type: mrr_at_3
|
| 1434 |
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value: 89.37899999999999
|
| 1435 |
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- type: mrr_at_5
|
| 1436 |
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value: 89.81
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| 1437 |
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|
| 1438 |
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value: 85.456
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| 1439 |
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|
| 1440 |
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value: 67.755
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| 1441 |
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|
| 1442 |
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value: 70.341
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| 1443 |
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- type: ndcg_at_1000
|
| 1444 |
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value: 71.538
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| 1445 |
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- type: ndcg_at_3
|
| 1446 |
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value: 63.735
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| 1447 |
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|
| 1448 |
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value: 65.823
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| 1449 |
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|
| 1450 |
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value: 85.456
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| 1451 |
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|
| 1452 |
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value: 13.450000000000001
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| 1453 |
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|
| 1454 |
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value: 1.545
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| 1455 |
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| 1456 |
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value: 0.16999999999999998
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| 1457 |
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|
| 1458 |
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value: 38.861000000000004
|
| 1459 |
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- type: precision_at_5
|
| 1460 |
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value: 24.964
|
| 1461 |
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- type: recall_at_1
|
| 1462 |
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value: 42.728
|
| 1463 |
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- type: recall_at_10
|
| 1464 |
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value: 67.252
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| 1465 |
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- type: recall_at_100
|
| 1466 |
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value: 77.265
|
| 1467 |
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- type: recall_at_1000
|
| 1468 |
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value: 85.246
|
| 1469 |
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- type: recall_at_3
|
| 1470 |
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value: 58.292
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| 1471 |
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- type: recall_at_5
|
| 1472 |
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value: 62.41100000000001
|
| 1473 |
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|
| 1474 |
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type: Classification
|
| 1475 |
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dataset:
|
| 1476 |
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type: mteb/imdb
|
| 1477 |
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name: MTEB ImdbClassification
|
| 1478 |
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config: default
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| 1479 |
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split: test
|
| 1480 |
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revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
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| 1481 |
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|
| 1482 |
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- type: accuracy
|
| 1483 |
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value: 87.4836
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| 1484 |
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- type: ap
|
| 1485 |
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value: 82.29552224030336
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| 1486 |
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- type: f1
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| 1487 |
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value: 87.42791432227448
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| 1488 |
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|
| 1489 |
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type: Retrieval
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| 1490 |
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dataset:
|
| 1491 |
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type: mteb/msmarco
|
| 1492 |
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name: MTEB MSMARCO
|
| 1493 |
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config: default
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| 1494 |
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split: dev
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| 1495 |
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revision: c5a29a104738b98a9e76336939199e264163d4a0
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| 1496 |
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metrics:
|
| 1497 |
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- type: map_at_1
|
| 1498 |
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value: 23.015
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| 1499 |
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- type: map_at_10
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| 1500 |
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value: 35.621
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| 1501 |
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| 1502 |
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value: 36.809
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| 1503 |
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| 1504 |
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value: 36.853
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| 1505 |
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| 1506 |
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value: 31.832
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| 1507 |
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| 1508 |
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value: 34.006
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| 1509 |
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| 1510 |
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value: 23.738999999999997
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| 1511 |
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| 1512 |
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value: 36.309999999999995
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| 1513 |
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- type: mrr_at_100
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| 1514 |
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value: 37.422
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| 1515 |
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- type: mrr_at_1000
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| 1516 |
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value: 37.461
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| 1517 |
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| 1518 |
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value: 32.592999999999996
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| 1519 |
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|
| 1520 |
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value: 34.736
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| 1521 |
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| 1522 |
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value: 23.724999999999998
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| 1523 |
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| 1524 |
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value: 42.617
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| 1525 |
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| 1526 |
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value: 48.217999999999996
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| 1527 |
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|
| 1528 |
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value: 49.309
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| 1529 |
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- type: ndcg_at_3
|
| 1530 |
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value: 34.905
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| 1531 |
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|
| 1532 |
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value: 38.769
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| 1533 |
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| 1534 |
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value: 23.724999999999998
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| 1535 |
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- type: precision_at_10
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| 1536 |
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value: 6.689
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| 1537 |
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- type: precision_at_100
|
| 1538 |
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value: 0.9480000000000001
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| 1539 |
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- type: precision_at_1000
|
| 1540 |
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value: 0.104
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| 1541 |
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- type: precision_at_3
|
| 1542 |
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value: 14.89
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| 1543 |
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- type: precision_at_5
|
| 1544 |
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value: 10.897
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| 1545 |
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- type: recall_at_1
|
| 1546 |
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value: 23.015
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| 1547 |
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- type: recall_at_10
|
| 1548 |
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value: 64.041
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| 1549 |
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- type: recall_at_100
|
| 1550 |
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value: 89.724
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| 1551 |
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- type: recall_at_1000
|
| 1552 |
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value: 98.00999999999999
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| 1553 |
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- type: recall_at_3
|
| 1554 |
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value: 43.064
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| 1555 |
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- type: recall_at_5
|
| 1556 |
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value: 52.31099999999999
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| 1557 |
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- task:
|
| 1558 |
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type: Classification
|
| 1559 |
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dataset:
|
| 1560 |
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type: mteb/mtop_domain
|
| 1561 |
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name: MTEB MTOPDomainClassification (en)
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| 1562 |
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config: en
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| 1563 |
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split: test
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| 1564 |
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revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
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| 1565 |
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metrics:
|
| 1566 |
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- type: accuracy
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| 1567 |
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value: 96.49794801641588
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| 1568 |
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- type: f1
|
| 1569 |
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value: 96.28931114498003
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| 1570 |
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|
| 1571 |
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type: Classification
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| 1572 |
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dataset:
|
| 1573 |
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type: mteb/mtop_intent
|
| 1574 |
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name: MTEB MTOPIntentClassification (en)
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| 1575 |
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config: en
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| 1576 |
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split: test
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| 1577 |
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revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
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| 1578 |
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metrics:
|
| 1579 |
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| 1580 |
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value: 82.81121751025992
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| 1581 |
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| 1582 |
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value: 63.18740125901853
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| 1583 |
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- task:
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| 1584 |
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type: Classification
|
| 1585 |
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dataset:
|
| 1586 |
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type: mteb/amazon_massive_intent
|
| 1587 |
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name: MTEB MassiveIntentClassification (en)
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| 1588 |
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config: en
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| 1589 |
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split: test
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| 1590 |
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revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
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| 1591 |
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metrics:
|
| 1592 |
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- type: accuracy
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| 1593 |
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value: 77.66644250168123
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| 1594 |
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| 1595 |
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value: 74.93211186867839
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| 1596 |
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- task:
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| 1597 |
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type: Classification
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| 1598 |
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dataset:
|
| 1599 |
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type: mteb/amazon_massive_scenario
|
| 1600 |
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name: MTEB MassiveScenarioClassification (en)
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| 1601 |
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config: en
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| 1602 |
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split: test
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| 1603 |
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
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| 1604 |
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metrics:
|
| 1605 |
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- type: accuracy
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| 1606 |
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value: 81.77202420981843
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| 1607 |
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- type: f1
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| 1608 |
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value: 81.63681969283554
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| 1609 |
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- task:
|
| 1610 |
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type: Clustering
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| 1611 |
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dataset:
|
| 1612 |
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type: mteb/medrxiv-clustering-p2p
|
| 1613 |
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name: MTEB MedrxivClusteringP2P
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| 1614 |
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config: default
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| 1615 |
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split: test
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| 1616 |
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revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
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| 1617 |
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metrics:
|
| 1618 |
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- type: v_measure
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| 1619 |
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value: 34.596687684870645
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| 1620 |
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- task:
|
| 1621 |
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type: Clustering
|
| 1622 |
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dataset:
|
| 1623 |
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type: mteb/medrxiv-clustering-s2s
|
| 1624 |
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name: MTEB MedrxivClusteringS2S
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| 1625 |
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config: default
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| 1626 |
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split: test
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| 1627 |
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revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
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| 1628 |
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metrics:
|
| 1629 |
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- type: v_measure
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| 1630 |
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value: 32.26965660101405
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| 1631 |
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- task:
|
| 1632 |
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type: Reranking
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| 1633 |
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dataset:
|
| 1634 |
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type: mteb/mind_small
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| 1635 |
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name: MTEB MindSmallReranking
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| 1636 |
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config: default
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| 1637 |
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split: test
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| 1638 |
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revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
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| 1639 |
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metrics:
|
| 1640 |
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- type: map
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| 1641 |
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value: 31.33619694846802
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| 1642 |
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| 1643 |
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| 1644 |
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| 1645 |
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| 1646 |
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dataset:
|
| 1647 |
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type: mteb/nfcorpus
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| 1648 |
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name: MTEB NFCorpus
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| 1649 |
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config: default
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| 1650 |
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split: test
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| 1651 |
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revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
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| 1652 |
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metrics:
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| 1653 |
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| 1654 |
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value: 6.0729999999999995
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value: 13.245999999999999
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| 1658 |
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value: 16.747999999999998
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value: 18.163
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value: 10.064
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value: 11.513
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| 1666 |
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value: 49.536
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| 1668 |
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value: 58.092
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| 1670 |
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value: 58.752
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| 1672 |
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value: 58.78
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| 1674 |
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| 1676 |
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value: 57.389
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value: 41.498000000000005
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value: 42.518
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value: 49.536
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value: 26.316
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value: 8.084
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value: 2.081
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| 1698 |
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value: 39.938
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| 1700 |
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value: 34.056
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| 1702 |
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value: 6.0729999999999995
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| 1704 |
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value: 16.593
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| 1706 |
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value: 32.883
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| 1708 |
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value: 64.654
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| 1710 |
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value: 11.174000000000001
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- type: recall_at_5
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| 1712 |
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value: 13.528
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| 1714 |
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type: Retrieval
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| 1715 |
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dataset:
|
| 1716 |
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type: mteb/nq
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| 1717 |
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name: MTEB NQ
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| 1718 |
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config: default
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| 1719 |
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split: test
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| 1720 |
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revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
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| 1721 |
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metrics:
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| 1722 |
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| 1723 |
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value: 30.043
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| 1725 |
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value: 45.318999999999996
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| 1726 |
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| 1727 |
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value: 46.381
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| 1728 |
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| 1729 |
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value: 46.412
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| 1731 |
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value: 40.941
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| 1733 |
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value: 43.662
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| 1734 |
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| 1735 |
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value: 33.98
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| 1737 |
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value: 47.870000000000005
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value: 48.681999999999995
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| 1741 |
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value: 48.703
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| 1743 |
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value: 44.341
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| 1745 |
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value: 46.547
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| 1747 |
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value: 33.98
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| 1749 |
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value: 52.957
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| 1751 |
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value: 57.434
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|
| 1753 |
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value: 58.103
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| 1754 |
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| 1755 |
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value: 44.896
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| 1757 |
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value: 49.353
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| 1759 |
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value: 33.98
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| 1761 |
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value: 8.786
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| 1762 |
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| 1763 |
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value: 1.1280000000000001
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| 1765 |
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value: 0.11900000000000001
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| 1767 |
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value: 20.577
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| 1769 |
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value: 14.942
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| 1770 |
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| 1771 |
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value: 30.043
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| 1773 |
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value: 73.593
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| 1774 |
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| 1775 |
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value: 93.026
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| 1776 |
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| 1777 |
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value: 97.943
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| 1778 |
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- type: recall_at_3
|
| 1779 |
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value: 52.955
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| 1780 |
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- type: recall_at_5
|
| 1781 |
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value: 63.132
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| 1782 |
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- task:
|
| 1783 |
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type: Retrieval
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| 1784 |
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dataset:
|
| 1785 |
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type: mteb/quora
|
| 1786 |
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name: MTEB QuoraRetrieval
|
| 1787 |
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config: default
|
| 1788 |
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split: test
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| 1789 |
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revision: None
|
| 1790 |
+
metrics:
|
| 1791 |
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- type: map_at_1
|
| 1792 |
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value: 70.808
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value: 84.675
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value: 85.322
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value: 85.33800000000001
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value: 81.68900000000001
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value: 83.543
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value: 81.5
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| 1806 |
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value: 87.59700000000001
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value: 87.705
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value: 87.70599999999999
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| 1811 |
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- type: mrr_at_3
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| 1812 |
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value: 86.607
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| 1813 |
+
- type: mrr_at_5
|
| 1814 |
+
value: 87.289
|
| 1815 |
+
- type: ndcg_at_1
|
| 1816 |
+
value: 81.51
|
| 1817 |
+
- type: ndcg_at_10
|
| 1818 |
+
value: 88.41799999999999
|
| 1819 |
+
- type: ndcg_at_100
|
| 1820 |
+
value: 89.644
|
| 1821 |
+
- type: ndcg_at_1000
|
| 1822 |
+
value: 89.725
|
| 1823 |
+
- type: ndcg_at_3
|
| 1824 |
+
value: 85.49900000000001
|
| 1825 |
+
- type: ndcg_at_5
|
| 1826 |
+
value: 87.078
|
| 1827 |
+
- type: precision_at_1
|
| 1828 |
+
value: 81.51
|
| 1829 |
+
- type: precision_at_10
|
| 1830 |
+
value: 13.438
|
| 1831 |
+
- type: precision_at_100
|
| 1832 |
+
value: 1.532
|
| 1833 |
+
- type: precision_at_1000
|
| 1834 |
+
value: 0.157
|
| 1835 |
+
- type: precision_at_3
|
| 1836 |
+
value: 37.363
|
| 1837 |
+
- type: precision_at_5
|
| 1838 |
+
value: 24.57
|
| 1839 |
+
- type: recall_at_1
|
| 1840 |
+
value: 70.808
|
| 1841 |
+
- type: recall_at_10
|
| 1842 |
+
value: 95.575
|
| 1843 |
+
- type: recall_at_100
|
| 1844 |
+
value: 99.667
|
| 1845 |
+
- type: recall_at_1000
|
| 1846 |
+
value: 99.98899999999999
|
| 1847 |
+
- type: recall_at_3
|
| 1848 |
+
value: 87.223
|
| 1849 |
+
- type: recall_at_5
|
| 1850 |
+
value: 91.682
|
| 1851 |
+
- task:
|
| 1852 |
+
type: Clustering
|
| 1853 |
+
dataset:
|
| 1854 |
+
type: mteb/reddit-clustering
|
| 1855 |
+
name: MTEB RedditClustering
|
| 1856 |
+
config: default
|
| 1857 |
+
split: test
|
| 1858 |
+
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
|
| 1859 |
+
metrics:
|
| 1860 |
+
- type: v_measure
|
| 1861 |
+
value: 58.614831329137715
|
| 1862 |
+
- task:
|
| 1863 |
+
type: Clustering
|
| 1864 |
+
dataset:
|
| 1865 |
+
type: mteb/reddit-clustering-p2p
|
| 1866 |
+
name: MTEB RedditClusteringP2P
|
| 1867 |
+
config: default
|
| 1868 |
+
split: test
|
| 1869 |
+
revision: 282350215ef01743dc01b456c7f5241fa8937f16
|
| 1870 |
+
metrics:
|
| 1871 |
+
- type: v_measure
|
| 1872 |
+
value: 66.86580408560826
|
| 1873 |
+
- task:
|
| 1874 |
+
type: Retrieval
|
| 1875 |
+
dataset:
|
| 1876 |
+
type: mteb/scidocs
|
| 1877 |
+
name: MTEB SCIDOCS
|
| 1878 |
+
config: default
|
| 1879 |
+
split: test
|
| 1880 |
+
revision: None
|
| 1881 |
+
metrics:
|
| 1882 |
+
- type: map_at_1
|
| 1883 |
+
value: 5.093
|
| 1884 |
+
- type: map_at_10
|
| 1885 |
+
value: 13.014000000000001
|
| 1886 |
+
- type: map_at_100
|
| 1887 |
+
value: 15.412999999999998
|
| 1888 |
+
- type: map_at_1000
|
| 1889 |
+
value: 15.756999999999998
|
| 1890 |
+
- type: map_at_3
|
| 1891 |
+
value: 9.216000000000001
|
| 1892 |
+
- type: map_at_5
|
| 1893 |
+
value: 11.036999999999999
|
| 1894 |
+
- type: mrr_at_1
|
| 1895 |
+
value: 25.1
|
| 1896 |
+
- type: mrr_at_10
|
| 1897 |
+
value: 37.133
|
| 1898 |
+
- type: mrr_at_100
|
| 1899 |
+
value: 38.165
|
| 1900 |
+
- type: mrr_at_1000
|
| 1901 |
+
value: 38.198
|
| 1902 |
+
- type: mrr_at_3
|
| 1903 |
+
value: 33.217
|
| 1904 |
+
- type: mrr_at_5
|
| 1905 |
+
value: 35.732
|
| 1906 |
+
- type: ndcg_at_1
|
| 1907 |
+
value: 25.1
|
| 1908 |
+
- type: ndcg_at_10
|
| 1909 |
+
value: 21.918000000000003
|
| 1910 |
+
- type: ndcg_at_100
|
| 1911 |
+
value: 30.983
|
| 1912 |
+
- type: ndcg_at_1000
|
| 1913 |
+
value: 36.629
|
| 1914 |
+
- type: ndcg_at_3
|
| 1915 |
+
value: 20.544999999999998
|
| 1916 |
+
- type: ndcg_at_5
|
| 1917 |
+
value: 18.192
|
| 1918 |
+
- type: precision_at_1
|
| 1919 |
+
value: 25.1
|
| 1920 |
+
- type: precision_at_10
|
| 1921 |
+
value: 11.44
|
| 1922 |
+
- type: precision_at_100
|
| 1923 |
+
value: 2.459
|
| 1924 |
+
- type: precision_at_1000
|
| 1925 |
+
value: 0.381
|
| 1926 |
+
- type: precision_at_3
|
| 1927 |
+
value: 19.267
|
| 1928 |
+
- type: precision_at_5
|
| 1929 |
+
value: 16.16
|
| 1930 |
+
- type: recall_at_1
|
| 1931 |
+
value: 5.093
|
| 1932 |
+
- type: recall_at_10
|
| 1933 |
+
value: 23.215
|
| 1934 |
+
- type: recall_at_100
|
| 1935 |
+
value: 49.902
|
| 1936 |
+
- type: recall_at_1000
|
| 1937 |
+
value: 77.403
|
| 1938 |
+
- type: recall_at_3
|
| 1939 |
+
value: 11.733
|
| 1940 |
+
- type: recall_at_5
|
| 1941 |
+
value: 16.372999999999998
|
| 1942 |
+
- task:
|
| 1943 |
+
type: STS
|
| 1944 |
+
dataset:
|
| 1945 |
+
type: mteb/sickr-sts
|
| 1946 |
+
name: MTEB SICK-R
|
| 1947 |
+
config: default
|
| 1948 |
+
split: test
|
| 1949 |
+
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
|
| 1950 |
+
metrics:
|
| 1951 |
+
- type: cos_sim_pearson
|
| 1952 |
+
value: 82.9365442977452
|
| 1953 |
+
- type: cos_sim_spearman
|
| 1954 |
+
value: 79.36960687383745
|
| 1955 |
+
- type: euclidean_pearson
|
| 1956 |
+
value: 79.6045204840714
|
| 1957 |
+
- type: euclidean_spearman
|
| 1958 |
+
value: 79.26382712751337
|
| 1959 |
+
- type: manhattan_pearson
|
| 1960 |
+
value: 79.4805084789529
|
| 1961 |
+
- type: manhattan_spearman
|
| 1962 |
+
value: 79.21847863209523
|
| 1963 |
+
- task:
|
| 1964 |
+
type: STS
|
| 1965 |
+
dataset:
|
| 1966 |
+
type: mteb/sts12-sts
|
| 1967 |
+
name: MTEB STS12
|
| 1968 |
+
config: default
|
| 1969 |
+
split: test
|
| 1970 |
+
revision: a0d554a64d88156834ff5ae9920b964011b16384
|
| 1971 |
+
metrics:
|
| 1972 |
+
- type: cos_sim_pearson
|
| 1973 |
+
value: 83.27906192961453
|
| 1974 |
+
- type: cos_sim_spearman
|
| 1975 |
+
value: 74.38364712099211
|
| 1976 |
+
- type: euclidean_pearson
|
| 1977 |
+
value: 78.54358927241223
|
| 1978 |
+
- type: euclidean_spearman
|
| 1979 |
+
value: 74.22185560806376
|
| 1980 |
+
- type: manhattan_pearson
|
| 1981 |
+
value: 78.50904327377751
|
| 1982 |
+
- type: manhattan_spearman
|
| 1983 |
+
value: 74.2627500781748
|
| 1984 |
+
- task:
|
| 1985 |
+
type: STS
|
| 1986 |
+
dataset:
|
| 1987 |
+
type: mteb/sts13-sts
|
| 1988 |
+
name: MTEB STS13
|
| 1989 |
+
config: default
|
| 1990 |
+
split: test
|
| 1991 |
+
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
|
| 1992 |
+
metrics:
|
| 1993 |
+
- type: cos_sim_pearson
|
| 1994 |
+
value: 84.66863742649639
|
| 1995 |
+
- type: cos_sim_spearman
|
| 1996 |
+
value: 84.70630905216271
|
| 1997 |
+
- type: euclidean_pearson
|
| 1998 |
+
value: 84.64498334705334
|
| 1999 |
+
- type: euclidean_spearman
|
| 2000 |
+
value: 84.87204770690148
|
| 2001 |
+
- type: manhattan_pearson
|
| 2002 |
+
value: 84.65774227976077
|
| 2003 |
+
- type: manhattan_spearman
|
| 2004 |
+
value: 84.91251851797985
|
| 2005 |
+
- task:
|
| 2006 |
+
type: STS
|
| 2007 |
+
dataset:
|
| 2008 |
+
type: mteb/sts14-sts
|
| 2009 |
+
name: MTEB STS14
|
| 2010 |
+
config: default
|
| 2011 |
+
split: test
|
| 2012 |
+
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
|
| 2013 |
+
metrics:
|
| 2014 |
+
- type: cos_sim_pearson
|
| 2015 |
+
value: 83.1577763924467
|
| 2016 |
+
- type: cos_sim_spearman
|
| 2017 |
+
value: 80.10314039230198
|
| 2018 |
+
- type: euclidean_pearson
|
| 2019 |
+
value: 81.51346991046043
|
| 2020 |
+
- type: euclidean_spearman
|
| 2021 |
+
value: 80.08678485109435
|
| 2022 |
+
- type: manhattan_pearson
|
| 2023 |
+
value: 81.57058914661894
|
| 2024 |
+
- type: manhattan_spearman
|
| 2025 |
+
value: 80.1516230725106
|
| 2026 |
+
- task:
|
| 2027 |
+
type: STS
|
| 2028 |
+
dataset:
|
| 2029 |
+
type: mteb/sts15-sts
|
| 2030 |
+
name: MTEB STS15
|
| 2031 |
+
config: default
|
| 2032 |
+
split: test
|
| 2033 |
+
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
|
| 2034 |
+
metrics:
|
| 2035 |
+
- type: cos_sim_pearson
|
| 2036 |
+
value: 86.40310839662533
|
| 2037 |
+
- type: cos_sim_spearman
|
| 2038 |
+
value: 87.16293477217867
|
| 2039 |
+
- type: euclidean_pearson
|
| 2040 |
+
value: 86.50688711184775
|
| 2041 |
+
- type: euclidean_spearman
|
| 2042 |
+
value: 87.08651444923031
|
| 2043 |
+
- type: manhattan_pearson
|
| 2044 |
+
value: 86.54674677557857
|
| 2045 |
+
- type: manhattan_spearman
|
| 2046 |
+
value: 87.15079017870971
|
| 2047 |
+
- task:
|
| 2048 |
+
type: STS
|
| 2049 |
+
dataset:
|
| 2050 |
+
type: mteb/sts16-sts
|
| 2051 |
+
name: MTEB STS16
|
| 2052 |
+
config: default
|
| 2053 |
+
split: test
|
| 2054 |
+
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
|
| 2055 |
+
metrics:
|
| 2056 |
+
- type: cos_sim_pearson
|
| 2057 |
+
value: 84.32886275207817
|
| 2058 |
+
- type: cos_sim_spearman
|
| 2059 |
+
value: 85.0190460590732
|
| 2060 |
+
- type: euclidean_pearson
|
| 2061 |
+
value: 84.42553652784679
|
| 2062 |
+
- type: euclidean_spearman
|
| 2063 |
+
value: 85.20027364279328
|
| 2064 |
+
- type: manhattan_pearson
|
| 2065 |
+
value: 84.42926246281078
|
| 2066 |
+
- type: manhattan_spearman
|
| 2067 |
+
value: 85.20187419804306
|
| 2068 |
+
- task:
|
| 2069 |
+
type: STS
|
| 2070 |
+
dataset:
|
| 2071 |
+
type: mteb/sts17-crosslingual-sts
|
| 2072 |
+
name: MTEB STS17 (en-en)
|
| 2073 |
+
config: en-en
|
| 2074 |
+
split: test
|
| 2075 |
+
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
|
| 2076 |
+
metrics:
|
| 2077 |
+
- type: cos_sim_pearson
|
| 2078 |
+
value: 90.76732216967812
|
| 2079 |
+
- type: cos_sim_spearman
|
| 2080 |
+
value: 90.63701653633909
|
| 2081 |
+
- type: euclidean_pearson
|
| 2082 |
+
value: 90.26678186114682
|
| 2083 |
+
- type: euclidean_spearman
|
| 2084 |
+
value: 90.67288073455427
|
| 2085 |
+
- type: manhattan_pearson
|
| 2086 |
+
value: 90.20772020584582
|
| 2087 |
+
- type: manhattan_spearman
|
| 2088 |
+
value: 90.60764863983702
|
| 2089 |
+
- task:
|
| 2090 |
+
type: STS
|
| 2091 |
+
dataset:
|
| 2092 |
+
type: mteb/sts22-crosslingual-sts
|
| 2093 |
+
name: MTEB STS22 (en)
|
| 2094 |
+
config: en
|
| 2095 |
+
split: test
|
| 2096 |
+
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
|
| 2097 |
+
metrics:
|
| 2098 |
+
- type: cos_sim_pearson
|
| 2099 |
+
value: 69.09280387698125
|
| 2100 |
+
- type: cos_sim_spearman
|
| 2101 |
+
value: 68.62743151172162
|
| 2102 |
+
- type: euclidean_pearson
|
| 2103 |
+
value: 69.89386398104689
|
| 2104 |
+
- type: euclidean_spearman
|
| 2105 |
+
value: 68.71191066733556
|
| 2106 |
+
- type: manhattan_pearson
|
| 2107 |
+
value: 69.92516500604872
|
| 2108 |
+
- type: manhattan_spearman
|
| 2109 |
+
value: 68.80452846992576
|
| 2110 |
+
- task:
|
| 2111 |
+
type: STS
|
| 2112 |
+
dataset:
|
| 2113 |
+
type: mteb/stsbenchmark-sts
|
| 2114 |
+
name: MTEB STSBenchmark
|
| 2115 |
+
config: default
|
| 2116 |
+
split: test
|
| 2117 |
+
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
|
| 2118 |
+
metrics:
|
| 2119 |
+
- type: cos_sim_pearson
|
| 2120 |
+
value: 86.13178592019887
|
| 2121 |
+
- type: cos_sim_spearman
|
| 2122 |
+
value: 86.03947178806887
|
| 2123 |
+
- type: euclidean_pearson
|
| 2124 |
+
value: 85.87029414285313
|
| 2125 |
+
- type: euclidean_spearman
|
| 2126 |
+
value: 86.04960843306998
|
| 2127 |
+
- type: manhattan_pearson
|
| 2128 |
+
value: 85.92946858580146
|
| 2129 |
+
- type: manhattan_spearman
|
| 2130 |
+
value: 86.12575341860442
|
| 2131 |
+
- task:
|
| 2132 |
+
type: Reranking
|
| 2133 |
+
dataset:
|
| 2134 |
+
type: mteb/scidocs-reranking
|
| 2135 |
+
name: MTEB SciDocsRR
|
| 2136 |
+
config: default
|
| 2137 |
+
split: test
|
| 2138 |
+
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
|
| 2139 |
+
metrics:
|
| 2140 |
+
- type: map
|
| 2141 |
+
value: 85.16657063002837
|
| 2142 |
+
- type: mrr
|
| 2143 |
+
value: 95.73671063867141
|
| 2144 |
+
- task:
|
| 2145 |
+
type: Retrieval
|
| 2146 |
+
dataset:
|
| 2147 |
+
type: mteb/scifact
|
| 2148 |
+
name: MTEB SciFact
|
| 2149 |
+
config: default
|
| 2150 |
+
split: test
|
| 2151 |
+
revision: 0228b52cf27578f30900b9e5271d331663a030d7
|
| 2152 |
+
metrics:
|
| 2153 |
+
- type: map_at_1
|
| 2154 |
+
value: 63.510999999999996
|
| 2155 |
+
- type: map_at_10
|
| 2156 |
+
value: 72.76899999999999
|
| 2157 |
+
- type: map_at_100
|
| 2158 |
+
value: 73.303
|
| 2159 |
+
- type: map_at_1000
|
| 2160 |
+
value: 73.32499999999999
|
| 2161 |
+
- type: map_at_3
|
| 2162 |
+
value: 70.514
|
| 2163 |
+
- type: map_at_5
|
| 2164 |
+
value: 71.929
|
| 2165 |
+
- type: mrr_at_1
|
| 2166 |
+
value: 66.333
|
| 2167 |
+
- type: mrr_at_10
|
| 2168 |
+
value: 73.75
|
| 2169 |
+
- type: mrr_at_100
|
| 2170 |
+
value: 74.119
|
| 2171 |
+
- type: mrr_at_1000
|
| 2172 |
+
value: 74.138
|
| 2173 |
+
- type: mrr_at_3
|
| 2174 |
+
value: 72.222
|
| 2175 |
+
- type: mrr_at_5
|
| 2176 |
+
value: 73.122
|
| 2177 |
+
- type: ndcg_at_1
|
| 2178 |
+
value: 66.333
|
| 2179 |
+
- type: ndcg_at_10
|
| 2180 |
+
value: 76.774
|
| 2181 |
+
- type: ndcg_at_100
|
| 2182 |
+
value: 78.78500000000001
|
| 2183 |
+
- type: ndcg_at_1000
|
| 2184 |
+
value: 79.254
|
| 2185 |
+
- type: ndcg_at_3
|
| 2186 |
+
value: 73.088
|
| 2187 |
+
- type: ndcg_at_5
|
| 2188 |
+
value: 75.002
|
| 2189 |
+
- type: precision_at_1
|
| 2190 |
+
value: 66.333
|
| 2191 |
+
- type: precision_at_10
|
| 2192 |
+
value: 9.833
|
| 2193 |
+
- type: precision_at_100
|
| 2194 |
+
value: 1.093
|
| 2195 |
+
- type: precision_at_1000
|
| 2196 |
+
value: 0.11299999999999999
|
| 2197 |
+
- type: precision_at_3
|
| 2198 |
+
value: 28.222
|
| 2199 |
+
- type: precision_at_5
|
| 2200 |
+
value: 18.333
|
| 2201 |
+
- type: recall_at_1
|
| 2202 |
+
value: 63.510999999999996
|
| 2203 |
+
- type: recall_at_10
|
| 2204 |
+
value: 87.98899999999999
|
| 2205 |
+
- type: recall_at_100
|
| 2206 |
+
value: 96.5
|
| 2207 |
+
- type: recall_at_1000
|
| 2208 |
+
value: 100.0
|
| 2209 |
+
- type: recall_at_3
|
| 2210 |
+
value: 77.86699999999999
|
| 2211 |
+
- type: recall_at_5
|
| 2212 |
+
value: 82.73899999999999
|
| 2213 |
+
- task:
|
| 2214 |
+
type: PairClassification
|
| 2215 |
+
dataset:
|
| 2216 |
+
type: mteb/sprintduplicatequestions-pairclassification
|
| 2217 |
+
name: MTEB SprintDuplicateQuestions
|
| 2218 |
+
config: default
|
| 2219 |
+
split: test
|
| 2220 |
+
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
|
| 2221 |
+
metrics:
|
| 2222 |
+
- type: cos_sim_accuracy
|
| 2223 |
+
value: 99.78514851485149
|
| 2224 |
+
- type: cos_sim_ap
|
| 2225 |
+
value: 94.94214383862038
|
| 2226 |
+
- type: cos_sim_f1
|
| 2227 |
+
value: 89.02255639097744
|
| 2228 |
+
- type: cos_sim_precision
|
| 2229 |
+
value: 89.2462311557789
|
| 2230 |
+
- type: cos_sim_recall
|
| 2231 |
+
value: 88.8
|
| 2232 |
+
- type: dot_accuracy
|
| 2233 |
+
value: 99.78217821782178
|
| 2234 |
+
- type: dot_ap
|
| 2235 |
+
value: 94.69965247836805
|
| 2236 |
+
- type: dot_f1
|
| 2237 |
+
value: 88.78695208970439
|
| 2238 |
+
- type: dot_precision
|
| 2239 |
+
value: 90.54054054054053
|
| 2240 |
+
- type: dot_recall
|
| 2241 |
+
value: 87.1
|
| 2242 |
+
- type: euclidean_accuracy
|
| 2243 |
+
value: 99.78118811881188
|
| 2244 |
+
- type: euclidean_ap
|
| 2245 |
+
value: 94.9865187695411
|
| 2246 |
+
- type: euclidean_f1
|
| 2247 |
+
value: 88.99950223992036
|
| 2248 |
+
- type: euclidean_precision
|
| 2249 |
+
value: 88.60257680872151
|
| 2250 |
+
- type: euclidean_recall
|
| 2251 |
+
value: 89.4
|
| 2252 |
+
- type: manhattan_accuracy
|
| 2253 |
+
value: 99.78811881188119
|
| 2254 |
+
- type: manhattan_ap
|
| 2255 |
+
value: 95.0021236766459
|
| 2256 |
+
- type: manhattan_f1
|
| 2257 |
+
value: 89.12071535022356
|
| 2258 |
+
- type: manhattan_precision
|
| 2259 |
+
value: 88.54886475814413
|
| 2260 |
+
- type: manhattan_recall
|
| 2261 |
+
value: 89.7
|
| 2262 |
+
- type: max_accuracy
|
| 2263 |
+
value: 99.78811881188119
|
| 2264 |
+
- type: max_ap
|
| 2265 |
+
value: 95.0021236766459
|
| 2266 |
+
- type: max_f1
|
| 2267 |
+
value: 89.12071535022356
|
| 2268 |
+
- task:
|
| 2269 |
+
type: Clustering
|
| 2270 |
+
dataset:
|
| 2271 |
+
type: mteb/stackexchange-clustering
|
| 2272 |
+
name: MTEB StackExchangeClustering
|
| 2273 |
+
config: default
|
| 2274 |
+
split: test
|
| 2275 |
+
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
|
| 2276 |
+
metrics:
|
| 2277 |
+
- type: v_measure
|
| 2278 |
+
value: 68.93190546593995
|
| 2279 |
+
- task:
|
| 2280 |
+
type: Clustering
|
| 2281 |
+
dataset:
|
| 2282 |
+
type: mteb/stackexchange-clustering-p2p
|
| 2283 |
+
name: MTEB StackExchangeClusteringP2P
|
| 2284 |
+
config: default
|
| 2285 |
+
split: test
|
| 2286 |
+
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
|
| 2287 |
+
metrics:
|
| 2288 |
+
- type: v_measure
|
| 2289 |
+
value: 37.602808534760655
|
| 2290 |
+
- task:
|
| 2291 |
+
type: Reranking
|
| 2292 |
+
dataset:
|
| 2293 |
+
type: mteb/stackoverflowdupquestions-reranking
|
| 2294 |
+
name: MTEB StackOverflowDupQuestions
|
| 2295 |
+
config: default
|
| 2296 |
+
split: test
|
| 2297 |
+
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
|
| 2298 |
+
metrics:
|
| 2299 |
+
- type: map
|
| 2300 |
+
value: 52.29214480978073
|
| 2301 |
+
- type: mrr
|
| 2302 |
+
value: 53.123169722434426
|
| 2303 |
+
- task:
|
| 2304 |
+
type: Summarization
|
| 2305 |
+
dataset:
|
| 2306 |
+
type: mteb/summeval
|
| 2307 |
+
name: MTEB SummEval
|
| 2308 |
+
config: default
|
| 2309 |
+
split: test
|
| 2310 |
+
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
|
| 2311 |
+
metrics:
|
| 2312 |
+
- type: cos_sim_pearson
|
| 2313 |
+
value: 30.967800769650022
|
| 2314 |
+
- type: cos_sim_spearman
|
| 2315 |
+
value: 31.168490040206926
|
| 2316 |
+
- type: dot_pearson
|
| 2317 |
+
value: 30.888603021128553
|
| 2318 |
+
- type: dot_spearman
|
| 2319 |
+
value: 31.028241262520385
|
| 2320 |
+
- task:
|
| 2321 |
+
type: Retrieval
|
| 2322 |
+
dataset:
|
| 2323 |
+
type: mteb/trec-covid
|
| 2324 |
+
name: MTEB TRECCOVID
|
| 2325 |
+
config: default
|
| 2326 |
+
split: test
|
| 2327 |
+
revision: None
|
| 2328 |
+
metrics:
|
| 2329 |
+
- type: map_at_1
|
| 2330 |
+
value: 0.22300000000000003
|
| 2331 |
+
- type: map_at_10
|
| 2332 |
+
value: 1.781
|
| 2333 |
+
- type: map_at_100
|
| 2334 |
+
value: 9.905999999999999
|
| 2335 |
+
- type: map_at_1000
|
| 2336 |
+
value: 23.455000000000002
|
| 2337 |
+
- type: map_at_3
|
| 2338 |
+
value: 0.569
|
| 2339 |
+
- type: map_at_5
|
| 2340 |
+
value: 0.918
|
| 2341 |
+
- type: mrr_at_1
|
| 2342 |
+
value: 84.0
|
| 2343 |
+
- type: mrr_at_10
|
| 2344 |
+
value: 91.067
|
| 2345 |
+
- type: mrr_at_100
|
| 2346 |
+
value: 91.067
|
| 2347 |
+
- type: mrr_at_1000
|
| 2348 |
+
value: 91.067
|
| 2349 |
+
- type: mrr_at_3
|
| 2350 |
+
value: 90.667
|
| 2351 |
+
- type: mrr_at_5
|
| 2352 |
+
value: 91.067
|
| 2353 |
+
- type: ndcg_at_1
|
| 2354 |
+
value: 78.0
|
| 2355 |
+
- type: ndcg_at_10
|
| 2356 |
+
value: 73.13499999999999
|
| 2357 |
+
- type: ndcg_at_100
|
| 2358 |
+
value: 55.32
|
| 2359 |
+
- type: ndcg_at_1000
|
| 2360 |
+
value: 49.532
|
| 2361 |
+
- type: ndcg_at_3
|
| 2362 |
+
value: 73.715
|
| 2363 |
+
- type: ndcg_at_5
|
| 2364 |
+
value: 72.74199999999999
|
| 2365 |
+
- type: precision_at_1
|
| 2366 |
+
value: 84.0
|
| 2367 |
+
- type: precision_at_10
|
| 2368 |
+
value: 78.8
|
| 2369 |
+
- type: precision_at_100
|
| 2370 |
+
value: 56.32
|
| 2371 |
+
- type: precision_at_1000
|
| 2372 |
+
value: 21.504
|
| 2373 |
+
- type: precision_at_3
|
| 2374 |
+
value: 77.333
|
| 2375 |
+
- type: precision_at_5
|
| 2376 |
+
value: 78.0
|
| 2377 |
+
- type: recall_at_1
|
| 2378 |
+
value: 0.22300000000000003
|
| 2379 |
+
- type: recall_at_10
|
| 2380 |
+
value: 2.049
|
| 2381 |
+
- type: recall_at_100
|
| 2382 |
+
value: 13.553
|
| 2383 |
+
- type: recall_at_1000
|
| 2384 |
+
value: 46.367999999999995
|
| 2385 |
+
- type: recall_at_3
|
| 2386 |
+
value: 0.604
|
| 2387 |
+
- type: recall_at_5
|
| 2388 |
+
value: 1.015
|
| 2389 |
+
- task:
|
| 2390 |
+
type: Retrieval
|
| 2391 |
+
dataset:
|
| 2392 |
+
type: mteb/touche2020
|
| 2393 |
+
name: MTEB Touche2020
|
| 2394 |
+
config: default
|
| 2395 |
+
split: test
|
| 2396 |
+
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
|
| 2397 |
+
metrics:
|
| 2398 |
+
- type: map_at_1
|
| 2399 |
+
value: 3.0380000000000003
|
| 2400 |
+
- type: map_at_10
|
| 2401 |
+
value: 10.188
|
| 2402 |
+
- type: map_at_100
|
| 2403 |
+
value: 16.395
|
| 2404 |
+
- type: map_at_1000
|
| 2405 |
+
value: 18.024
|
| 2406 |
+
- type: map_at_3
|
| 2407 |
+
value: 6.236
|
| 2408 |
+
- type: map_at_5
|
| 2409 |
+
value: 7.276000000000001
|
| 2410 |
+
- type: mrr_at_1
|
| 2411 |
+
value: 34.694
|
| 2412 |
+
- type: mrr_at_10
|
| 2413 |
+
value: 46.292
|
| 2414 |
+
- type: mrr_at_100
|
| 2415 |
+
value: 47.446
|
| 2416 |
+
- type: mrr_at_1000
|
| 2417 |
+
value: 47.446
|
| 2418 |
+
- type: mrr_at_3
|
| 2419 |
+
value: 41.156
|
| 2420 |
+
- type: mrr_at_5
|
| 2421 |
+
value: 44.32
|
| 2422 |
+
- type: ndcg_at_1
|
| 2423 |
+
value: 32.653
|
| 2424 |
+
- type: ndcg_at_10
|
| 2425 |
+
value: 25.219
|
| 2426 |
+
- type: ndcg_at_100
|
| 2427 |
+
value: 37.802
|
| 2428 |
+
- type: ndcg_at_1000
|
| 2429 |
+
value: 49.274
|
| 2430 |
+
- type: ndcg_at_3
|
| 2431 |
+
value: 28.605999999999998
|
| 2432 |
+
- type: ndcg_at_5
|
| 2433 |
+
value: 26.21
|
| 2434 |
+
- type: precision_at_1
|
| 2435 |
+
value: 34.694
|
| 2436 |
+
- type: precision_at_10
|
| 2437 |
+
value: 21.837
|
| 2438 |
+
- type: precision_at_100
|
| 2439 |
+
value: 7.776
|
| 2440 |
+
- type: precision_at_1000
|
| 2441 |
+
value: 1.522
|
| 2442 |
+
- type: precision_at_3
|
| 2443 |
+
value: 28.571
|
| 2444 |
+
- type: precision_at_5
|
| 2445 |
+
value: 25.306
|
| 2446 |
+
- type: recall_at_1
|
| 2447 |
+
value: 3.0380000000000003
|
| 2448 |
+
- type: recall_at_10
|
| 2449 |
+
value: 16.298000000000002
|
| 2450 |
+
- type: recall_at_100
|
| 2451 |
+
value: 48.712
|
| 2452 |
+
- type: recall_at_1000
|
| 2453 |
+
value: 83.16799999999999
|
| 2454 |
+
- type: recall_at_3
|
| 2455 |
+
value: 7.265000000000001
|
| 2456 |
+
- type: recall_at_5
|
| 2457 |
+
value: 9.551
|
| 2458 |
+
- task:
|
| 2459 |
+
type: Classification
|
| 2460 |
+
dataset:
|
| 2461 |
+
type: mteb/toxic_conversations_50k
|
| 2462 |
+
name: MTEB ToxicConversationsClassification
|
| 2463 |
+
config: default
|
| 2464 |
+
split: test
|
| 2465 |
+
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
|
| 2466 |
+
metrics:
|
| 2467 |
+
- type: accuracy
|
| 2468 |
+
value: 83.978
|
| 2469 |
+
- type: ap
|
| 2470 |
+
value: 24.751887949330015
|
| 2471 |
+
- type: f1
|
| 2472 |
+
value: 66.8685134049279
|
| 2473 |
+
- task:
|
| 2474 |
+
type: Classification
|
| 2475 |
+
dataset:
|
| 2476 |
+
type: mteb/tweet_sentiment_extraction
|
| 2477 |
+
name: MTEB TweetSentimentExtractionClassification
|
| 2478 |
+
config: default
|
| 2479 |
+
split: test
|
| 2480 |
+
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
|
| 2481 |
+
metrics:
|
| 2482 |
+
- type: accuracy
|
| 2483 |
+
value: 61.573288058856825
|
| 2484 |
+
- type: f1
|
| 2485 |
+
value: 61.973261751726604
|
| 2486 |
+
- task:
|
| 2487 |
+
type: Clustering
|
| 2488 |
+
dataset:
|
| 2489 |
+
type: mteb/twentynewsgroups-clustering
|
| 2490 |
+
name: MTEB TwentyNewsgroupsClustering
|
| 2491 |
+
config: default
|
| 2492 |
+
split: test
|
| 2493 |
+
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
|
| 2494 |
+
metrics:
|
| 2495 |
+
- type: v_measure
|
| 2496 |
+
value: 48.75483298792469
|
| 2497 |
+
- task:
|
| 2498 |
+
type: PairClassification
|
| 2499 |
+
dataset:
|
| 2500 |
+
type: mteb/twittersemeval2015-pairclassification
|
| 2501 |
+
name: MTEB TwitterSemEval2015
|
| 2502 |
+
config: default
|
| 2503 |
+
split: test
|
| 2504 |
+
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
|
| 2505 |
+
metrics:
|
| 2506 |
+
- type: cos_sim_accuracy
|
| 2507 |
+
value: 86.36824223639506
|
| 2508 |
+
- type: cos_sim_ap
|
| 2509 |
+
value: 75.53126388573047
|
| 2510 |
+
- type: cos_sim_f1
|
| 2511 |
+
value: 67.9912831688245
|
| 2512 |
+
- type: cos_sim_precision
|
| 2513 |
+
value: 66.11817501869858
|
| 2514 |
+
- type: cos_sim_recall
|
| 2515 |
+
value: 69.9736147757256
|
| 2516 |
+
- type: dot_accuracy
|
| 2517 |
+
value: 86.39804494248078
|
| 2518 |
+
- type: dot_ap
|
| 2519 |
+
value: 75.27598891718046
|
| 2520 |
+
- type: dot_f1
|
| 2521 |
+
value: 67.91146284159763
|
| 2522 |
+
- type: dot_precision
|
| 2523 |
+
value: 63.90505003490807
|
| 2524 |
+
- type: dot_recall
|
| 2525 |
+
value: 72.45382585751979
|
| 2526 |
+
- type: euclidean_accuracy
|
| 2527 |
+
value: 86.36228169517793
|
| 2528 |
+
- type: euclidean_ap
|
| 2529 |
+
value: 75.51438087434647
|
| 2530 |
+
- type: euclidean_f1
|
| 2531 |
+
value: 68.02370523061066
|
| 2532 |
+
- type: euclidean_precision
|
| 2533 |
+
value: 66.46525679758308
|
| 2534 |
+
- type: euclidean_recall
|
| 2535 |
+
value: 69.65699208443272
|
| 2536 |
+
- type: manhattan_accuracy
|
| 2537 |
+
value: 86.46361089586935
|
| 2538 |
+
- type: manhattan_ap
|
| 2539 |
+
value: 75.50800785730111
|
| 2540 |
+
- type: manhattan_f1
|
| 2541 |
+
value: 67.9220437187253
|
| 2542 |
+
- type: manhattan_precision
|
| 2543 |
+
value: 67.79705573080967
|
| 2544 |
+
- type: manhattan_recall
|
| 2545 |
+
value: 68.04749340369392
|
| 2546 |
+
- type: max_accuracy
|
| 2547 |
+
value: 86.46361089586935
|
| 2548 |
+
- type: max_ap
|
| 2549 |
+
value: 75.53126388573047
|
| 2550 |
+
- type: max_f1
|
| 2551 |
+
value: 68.02370523061066
|
| 2552 |
+
- task:
|
| 2553 |
+
type: PairClassification
|
| 2554 |
+
dataset:
|
| 2555 |
+
type: mteb/twitterurlcorpus-pairclassification
|
| 2556 |
+
name: MTEB TwitterURLCorpus
|
| 2557 |
+
config: default
|
| 2558 |
+
split: test
|
| 2559 |
+
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
|
| 2560 |
+
metrics:
|
| 2561 |
+
- type: cos_sim_accuracy
|
| 2562 |
+
value: 88.80350836341057
|
| 2563 |
+
- type: cos_sim_ap
|
| 2564 |
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value: 85.51101933260743
|
| 2565 |
+
- type: cos_sim_f1
|
| 2566 |
+
value: 77.9152271629704
|
| 2567 |
+
- type: cos_sim_precision
|
| 2568 |
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value: 75.27815662910056
|
| 2569 |
+
- type: cos_sim_recall
|
| 2570 |
+
value: 80.74376347397599
|
| 2571 |
+
- type: dot_accuracy
|
| 2572 |
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value: 88.84425815966158
|
| 2573 |
+
- type: dot_ap
|
| 2574 |
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value: 85.49726945962519
|
| 2575 |
+
- type: dot_f1
|
| 2576 |
+
value: 77.94445269567801
|
| 2577 |
+
- type: dot_precision
|
| 2578 |
+
value: 75.27251864601261
|
| 2579 |
+
- type: dot_recall
|
| 2580 |
+
value: 80.81305820757623
|
| 2581 |
+
- type: euclidean_accuracy
|
| 2582 |
+
value: 88.80350836341057
|
| 2583 |
+
- type: euclidean_ap
|
| 2584 |
+
value: 85.4882880790211
|
| 2585 |
+
- type: euclidean_f1
|
| 2586 |
+
value: 77.87063284615103
|
| 2587 |
+
- type: euclidean_precision
|
| 2588 |
+
value: 74.61022927689595
|
| 2589 |
+
- type: euclidean_recall
|
| 2590 |
+
value: 81.42901139513397
|
| 2591 |
+
- type: manhattan_accuracy
|
| 2592 |
+
value: 88.7161873714441
|
| 2593 |
+
- type: manhattan_ap
|
| 2594 |
+
value: 85.45753871906821
|
| 2595 |
+
- type: manhattan_f1
|
| 2596 |
+
value: 77.8686401480111
|
| 2597 |
+
- type: manhattan_precision
|
| 2598 |
+
value: 74.95903683123174
|
| 2599 |
+
- type: manhattan_recall
|
| 2600 |
+
value: 81.01324299353249
|
| 2601 |
+
- type: max_accuracy
|
| 2602 |
+
value: 88.84425815966158
|
| 2603 |
+
- type: max_ap
|
| 2604 |
+
value: 85.51101933260743
|
| 2605 |
+
- type: max_f1
|
| 2606 |
+
value: 77.94445269567801
|
| 2607 |
---
|
| 2608 |
+
|
| 2609 |
+
<!-- **English** | [中文](./README_zh.md) -->
|
| 2610 |
|
| 2611 |
+
# gte-base-en-v1.5
|
| 2612 |
|
| 2613 |
+
We introduce `gte-v1.5` series, upgraded `gte` embeddings that support the context length of up to **8192**, while further enhancing model performance.
|
| 2614 |
+
The models are built upon the `transformer++` encoder [backbone](https://huggingface.co/Alibaba-NLP/new-impl) (BERT + RoPE + GLU).
|
| 2615 |
|
| 2616 |
+
The `gte-v1.5` series achieve state-of-the-art scores on the MTEB benchmark within the same model size category and prodvide competitive on the LoCo long-context retrieval tests (refer to [Evaluation](#evaluation)).
|
| 2617 |
|
| 2618 |
+
We also present the [`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct),
|
| 2619 |
+
a SOTA instruction-tuned multi-lingual embedding model that ranked 2nd in MTEB and 1st in C-MTEB.
|
|
|
|
|
|
|
| 2620 |
|
| 2621 |
<!-- Provide a longer summary of what this model is. -->
|
| 2622 |
|
| 2623 |
+
- **Developed by:** Institute for Intelligent Computing, Alibaba Group
|
| 2624 |
+
- **Model type:** Text Embeddings
|
| 2625 |
+
- **Paper:** [mGTE: Generalized Long-Context Text Representation and Reranking
|
| 2626 |
+
Models for Multilingual Text Retrieval](https://arxiv.org/pdf/2407.19669)
|
| 2627 |
|
| 2628 |
+
<!-- - **Demo [optional]:** [More Information Needed] -->
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 2629 |
|
| 2630 |
+
### Model list
|
| 2631 |
|
| 2632 |
+
| Models | Language | Model Size | Max Seq. Length | Dimension | MTEB-en | LoCo |
|
| 2633 |
+
|:-----: | :-----: |:-----: |:-----: |:-----: | :-----: | :-----: |
|
| 2634 |
+
|[`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct)| Multiple | 7720 | 32768 | 4096 | 67.34 | 87.57 |
|
| 2635 |
+
|[`gte-large-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | English | 434 | 8192 | 1024 | 65.39 | 86.71 |
|
| 2636 |
+
|[`gte-base-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | English | 137 | 8192 | 768 | 64.11 | 87.44 |
|
| 2637 |
|
|
|
|
|
|
|
|
|
|
| 2638 |
|
| 2639 |
+
## How to Get Started with the Model
|
| 2640 |
|
| 2641 |
+
Use the code below to get started with the model.
|
| 2642 |
|
| 2643 |
+
```python
|
| 2644 |
+
# Requires transformers>=4.36.0
|
| 2645 |
|
| 2646 |
+
import torch.nn.functional as F
|
| 2647 |
+
from transformers import AutoModel, AutoTokenizer
|
| 2648 |
|
| 2649 |
+
input_texts = [
|
| 2650 |
+
"what is the capital of China?",
|
| 2651 |
+
"how to implement quick sort in python?",
|
| 2652 |
+
"Beijing",
|
| 2653 |
+
"sorting algorithms"
|
| 2654 |
+
]
|
| 2655 |
|
| 2656 |
+
model_path = 'Alibaba-NLP/gte-base-en-v1.5'
|
| 2657 |
+
tokenizer = AutoTokenizer.from_pretrained(model_path)
|
| 2658 |
+
model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
|
| 2659 |
|
| 2660 |
+
# Tokenize the input texts
|
| 2661 |
+
batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt')
|
| 2662 |
|
| 2663 |
+
outputs = model(**batch_dict)
|
| 2664 |
+
embeddings = outputs.last_hidden_state[:, 0]
|
| 2665 |
+
|
| 2666 |
+
# (Optionally) normalize embeddings
|
| 2667 |
+
embeddings = F.normalize(embeddings, p=2, dim=1)
|
| 2668 |
+
scores = (embeddings[:1] @ embeddings[1:].T) * 100
|
| 2669 |
+
print(scores.tolist())
|
| 2670 |
+
```
|
| 2671 |
|
| 2672 |
+
**It is recommended to install xformers and enable unpadding for acceleration, refer to [enable-unpadding-and-xformers](https://huggingface.co/Alibaba-NLP/new-impl#recommendation-enable-unpadding-and-acceleration-with-xformers).**
|
| 2673 |
|
|
|
|
| 2674 |
|
| 2675 |
+
Use with `sentence-transformers`:
|
| 2676 |
|
| 2677 |
+
```python
|
| 2678 |
+
# Requires sentence_transformers>=2.7.0
|
| 2679 |
|
| 2680 |
+
from sentence_transformers import SentenceTransformer
|
| 2681 |
+
from sentence_transformers.util import cos_sim
|
| 2682 |
|
| 2683 |
+
sentences = ['That is a happy person', 'That is a very happy person']
|
| 2684 |
|
| 2685 |
+
model = SentenceTransformer('Alibaba-NLP/gte-base-en-v1.5', trust_remote_code=True)
|
| 2686 |
+
embeddings = model.encode(sentences)
|
| 2687 |
+
print(cos_sim(embeddings[0], embeddings[1]))
|
| 2688 |
+
```
|
| 2689 |
|
| 2690 |
+
Use with `transformers.js`:
|
| 2691 |
|
| 2692 |
+
```js
|
| 2693 |
+
// npm i @xenova/transformers
|
| 2694 |
+
import { pipeline, dot } from '@xenova/transformers';
|
| 2695 |
|
| 2696 |
+
// Create feature extraction pipeline
|
| 2697 |
+
const extractor = await pipeline('feature-extraction', 'Alibaba-NLP/gte-base-en-v1.5', {
|
| 2698 |
+
quantized: false, // Comment out this line to use the quantized version
|
| 2699 |
+
});
|
| 2700 |
|
| 2701 |
+
// Generate sentence embeddings
|
| 2702 |
+
const sentences = [
|
| 2703 |
+
"what is the capital of China?",
|
| 2704 |
+
"how to implement quick sort in python?",
|
| 2705 |
+
"Beijing",
|
| 2706 |
+
"sorting algorithms"
|
| 2707 |
+
]
|
| 2708 |
+
const output = await extractor(sentences, { normalize: true, pooling: 'cls' });
|
| 2709 |
|
| 2710 |
+
// Compute similarity scores
|
| 2711 |
+
const [source_embeddings, ...document_embeddings ] = output.tolist();
|
| 2712 |
+
const similarities = document_embeddings.map(x => 100 * dot(source_embeddings, x));
|
| 2713 |
+
console.log(similarities); // [34.504930869007296, 64.03973265120138, 19.520042686034362]
|
| 2714 |
+
```
|
| 2715 |
|
| 2716 |
## Training Details
|
| 2717 |
|
| 2718 |
### Training Data
|
| 2719 |
|
| 2720 |
+
- Masked language modeling (MLM): `c4-en`
|
| 2721 |
+
- Weak-supervised contrastive pre-training (CPT): [GTE](https://arxiv.org/pdf/2308.03281.pdf) pre-training data
|
| 2722 |
+
- Supervised contrastive fine-tuning: [GTE](https://arxiv.org/pdf/2308.03281.pdf) fine-tuning data
|
| 2723 |
|
| 2724 |
### Training Procedure
|
| 2725 |
|
| 2726 |
+
To enable the backbone model to support a context length of 8192, we adopted a multi-stage training strategy.
|
| 2727 |
+
The model first undergoes preliminary MLM pre-training on shorter lengths.
|
| 2728 |
+
And then, we resample the data, reducing the proportion of short texts, and continue the MLM pre-training.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 2729 |
|
| 2730 |
+
The entire training process is as follows:
|
| 2731 |
+
- MLM-2048: lr 5e-4, mlm_probability 0.3, batch_size 4096, num_steps 70000, rope_base 10000
|
| 2732 |
+
- [MLM-8192](https://huggingface.co/Alibaba-NLP/gte-en-mlm-base): lr 5e-5, mlm_probability 0.3, batch_size 1024, num_steps 20000, rope_base 500000
|
| 2733 |
+
- CPT: max_len 512, lr 2e-4, batch_size 32768, num_steps 100000
|
| 2734 |
+
- Fine-tuning: TODO
|
| 2735 |
|
|
|
|
|
|
|
|
|
|
| 2736 |
|
| 2737 |
## Evaluation
|
| 2738 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 2739 |
|
| 2740 |
+
### MTEB
|
| 2741 |
|
| 2742 |
+
The results of other models are retrieved from [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
|
| 2743 |
|
| 2744 |
+
The gte evaluation setting: `mteb==1.2.0, fp16 auto mix precision, max_length=8192`, and set ntk scaling factor to 2 (equivalent to rope_base * 2).
|
| 2745 |
|
| 2746 |
+
| Model Name | Param Size (M) | Dimension | Sequence Length | Average (56) | Class. (12) | Clust. (11) | Pair Class. (3) | Reran. (4) | Retr. (15) | STS (10) | Summ. (1) |
|
| 2747 |
+
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|
| 2748 |
+
| [**gte-large-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 434 | 1024 | 8192 | **65.39** | 77.75 | 47.95 | 84.63 | 58.50 | 57.91 | 81.43 | 30.91 |
|
| 2749 |
+
| [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) | 335 | 1024 | 512 | 64.68 | 75.64 | 46.71 | 87.2 | 60.11 | 54.39 | 85 | 32.71 |
|
| 2750 |
+
| [multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) | 560 | 1024 | 514 | 64.41 | 77.56 | 47.1 | 86.19 | 58.58 | 52.47 | 84.78 | 30.39 |
|
| 2751 |
+
| [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5)| 335 | 1024 | 512 | 64.23 | 75.97 | 46.08 | 87.12 | 60.03 | 54.29 | 83.11 | 31.61 |
|
| 2752 |
+
| [**gte-base-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | 137 | 768 | 8192 | **64.11** | 77.17 | 46.82 | 85.33 | 57.66 | 54.09 | 81.97 | 31.17 |
|
| 2753 |
+
| [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)| 109 | 768 | 512 | 63.55 | 75.53 | 45.77 | 86.55 | 58.86 | 53.25 | 82.4 | 31.07 |
|
| 2754 |
|
|
|
|
| 2755 |
|
| 2756 |
+
### LoCo
|
| 2757 |
|
| 2758 |
+
| Model Name | Dimension | Sequence Length | Average (5) | QsmsumRetrieval | SummScreenRetrieval | QasperAbastractRetrieval | QasperTitleRetrieval | GovReportRetrieval |
|
| 2759 |
+
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|
| 2760 |
+
| [gte-qwen1.5-7b](https://huggingface.co/Alibaba-NLP/gte-qwen1.5-7b) | 4096 | 32768 | 87.57 | 49.37 | 93.10 | 99.67 | 97.54 | 98.21 |
|
| 2761 |
+
| [gte-large-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-v1.5) |1024 | 8192 | 86.71 | 44.55 | 92.61 | 99.82 | 97.81 | 98.74 |
|
| 2762 |
+
| [gte-base-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-v1.5) | 768 | 8192 | 87.44 | 49.91 | 91.78 | 99.82 | 97.13 | 98.58 |
|
| 2763 |
|
|
|
|
| 2764 |
|
|
|
|
| 2765 |
|
| 2766 |
+
## Citation
|
| 2767 |
+
If you find our paper or models helpful, please consider citing them as follows:
|
| 2768 |
|
| 2769 |
+
```
|
| 2770 |
+
@misc{zhang2024mgte,
|
| 2771 |
+
title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval},
|
| 2772 |
+
author={Xin Zhang and Yanzhao Zhang and Dingkun Long and Wen Xie and Ziqi Dai and Jialong Tang and Huan Lin and Baosong Yang and Pengjun Xie and Fei Huang and Meishan Zhang and Wenjie Li and Min Zhang},
|
| 2773 |
+
year={2024},
|
| 2774 |
+
eprint={2407.19669},
|
| 2775 |
+
archivePrefix={arXiv},
|
| 2776 |
+
primaryClass={cs.CL},
|
| 2777 |
+
url={https://arxiv.org/abs/2407.19669},
|
| 2778 |
+
}
|
| 2779 |
+
@misc{li2023gte,
|
| 2780 |
+
title={Towards General Text Embeddings with Multi-stage Contrastive Learning},
|
| 2781 |
+
author={Zehan Li and Xin Zhang and Yanzhao Zhang and Dingkun Long and Pengjun Xie and Meishan Zhang},
|
| 2782 |
+
year={2023},
|
| 2783 |
+
eprint={2308.03281},
|
| 2784 |
+
archivePrefix={arXiv},
|
| 2785 |
+
primaryClass={cs.CL},
|
| 2786 |
+
url={https://arxiv.org/abs/2308.03281},
|
| 2787 |
+
}
|
| 2788 |
+
```
|
config.json
CHANGED
|
@@ -5,7 +5,7 @@
|
|
| 5 |
],
|
| 6 |
"attention_probs_dropout_prob": 0.0,
|
| 7 |
"auto_map": {
|
| 8 |
-
"AutoConfig": "
|
| 9 |
"AutoModel": "Alibaba-NLP/new-impl--modeling.NewModel",
|
| 10 |
"AutoModelForMaskedLM": "Alibaba-NLP/new-impl--modeling.NewForMaskedLM",
|
| 11 |
"AutoModelForMultipleChoice": "Alibaba-NLP/new-impl--modeling.NewForMultipleChoice",
|
|
|
|
| 5 |
],
|
| 6 |
"attention_probs_dropout_prob": 0.0,
|
| 7 |
"auto_map": {
|
| 8 |
+
"AutoConfig": "configuration.NewConfig",
|
| 9 |
"AutoModel": "Alibaba-NLP/new-impl--modeling.NewModel",
|
| 10 |
"AutoModelForMaskedLM": "Alibaba-NLP/new-impl--modeling.NewForMaskedLM",
|
| 11 |
"AutoModelForMultipleChoice": "Alibaba-NLP/new-impl--modeling.NewForMultipleChoice",
|
config_sentence_transformers.json
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"__version__": {
|
| 3 |
+
"sentence_transformers": "3.2.1",
|
| 4 |
+
"transformers": "4.46.0",
|
| 5 |
+
"pytorch": "2.5.0"
|
| 6 |
+
},
|
| 7 |
+
"prompts": {},
|
| 8 |
+
"default_prompt_name": null,
|
| 9 |
+
"similarity_fn_name": null
|
| 10 |
+
}
|
configuration.py
ADDED
|
@@ -0,0 +1,145 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
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|
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|
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|
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|
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|
|
|
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|
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|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
| 1 |
+
# coding=utf-8
|
| 2 |
+
# Copyright 2024 The GTE Team Authors and Alibaba Group.
|
| 3 |
+
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
|
| 4 |
+
#
|
| 5 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 6 |
+
# you may not use this file except in compliance with the License.
|
| 7 |
+
# You may obtain a copy of the License at
|
| 8 |
+
#
|
| 9 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
| 10 |
+
#
|
| 11 |
+
# Unless required by applicable law or agreed to in writing, software
|
| 12 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 13 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 14 |
+
# See the License for the specific language governing permissions and
|
| 15 |
+
# limitations under the License.
|
| 16 |
+
""" NEW model configuration"""
|
| 17 |
+
from transformers.configuration_utils import PretrainedConfig
|
| 18 |
+
from transformers.utils import logging
|
| 19 |
+
|
| 20 |
+
logger = logging.get_logger(__name__)
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
class NewConfig(PretrainedConfig):
|
| 24 |
+
r"""
|
| 25 |
+
This is the configuration class to store the configuration of a [`NewModel`] or a [`TFNewModel`]. It is used to
|
| 26 |
+
instantiate a NEW model according to the specified arguments, defining the model architecture. Instantiating a
|
| 27 |
+
configuration with the defaults will yield a similar configuration to that of the NEW
|
| 28 |
+
[izhx/new-base-en](https://huggingface.co/izhx/new-base-en) architecture.
|
| 29 |
+
|
| 30 |
+
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
| 31 |
+
documentation from [`PretrainedConfig`] for more information.
|
| 32 |
+
|
| 33 |
+
|
| 34 |
+
Args:
|
| 35 |
+
vocab_size (`int`, *optional*, defaults to 30522):
|
| 36 |
+
Vocabulary size of the NEW model. Defines the number of different tokens that can be represented by the
|
| 37 |
+
`inputs_ids` passed when calling [`NewModel`] or [`TFNewModel`].
|
| 38 |
+
hidden_size (`int`, *optional*, defaults to 768):
|
| 39 |
+
Dimensionality of the encoder layers and the pooler layer.
|
| 40 |
+
num_hidden_layers (`int`, *optional*, defaults to 12):
|
| 41 |
+
Number of hidden layers in the Transformer encoder.
|
| 42 |
+
num_attention_heads (`int`, *optional*, defaults to 12):
|
| 43 |
+
Number of attention heads for each attention layer in the Transformer encoder.
|
| 44 |
+
intermediate_size (`int`, *optional*, defaults to 3072):
|
| 45 |
+
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
|
| 46 |
+
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
|
| 47 |
+
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
|
| 48 |
+
`"relu"`, `"silu"` and `"gelu_new"` are supported.
|
| 49 |
+
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
|
| 50 |
+
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
|
| 51 |
+
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
|
| 52 |
+
The dropout ratio for the attention probabilities.
|
| 53 |
+
max_position_embeddings (`int`, *optional*, defaults to 512):
|
| 54 |
+
The maximum sequence length that this model might ever be used with. Typically set this to something large
|
| 55 |
+
just in case (e.g., 512 or 1024 or 2048).
|
| 56 |
+
type_vocab_size (`int`, *optional*, defaults to 2):
|
| 57 |
+
The vocabulary size of the `token_type_ids` passed when calling [`NewModel`] or [`TFNewModel`].
|
| 58 |
+
initializer_range (`float`, *optional*, defaults to 0.02):
|
| 59 |
+
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
| 60 |
+
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
|
| 61 |
+
The epsilon used by the layer normalization layers.
|
| 62 |
+
position_embedding_type (`str`, *optional*, defaults to `"rope"`):
|
| 63 |
+
Type of position embedding. Choose one of `"absolute"`, `"rope"`.
|
| 64 |
+
rope_theta (`float`, *optional*, defaults to 10000.0):
|
| 65 |
+
The base period of the RoPE embeddings.
|
| 66 |
+
rope_scaling (`Dict`, *optional*):
|
| 67 |
+
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
|
| 68 |
+
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
|
| 69 |
+
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
|
| 70 |
+
`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
|
| 71 |
+
these scaling strategies behave:
|
| 72 |
+
https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
|
| 73 |
+
experimental feature, subject to breaking API changes in future versions.
|
| 74 |
+
classifier_dropout (`float`, *optional*):
|
| 75 |
+
The dropout ratio for the classification head.
|
| 76 |
+
|
| 77 |
+
Examples:
|
| 78 |
+
|
| 79 |
+
```python
|
| 80 |
+
>>> from transformers import NewConfig, NewModel
|
| 81 |
+
|
| 82 |
+
>>> # Initializing a NEW izhx/new-base-en style configuration
|
| 83 |
+
>>> configuration = NewConfig()
|
| 84 |
+
|
| 85 |
+
>>> # Initializing a model (with random weights) from the izhx/new-base-en style configuration
|
| 86 |
+
>>> model = NewModel(configuration)
|
| 87 |
+
|
| 88 |
+
>>> # Accessing the model configuration
|
| 89 |
+
>>> configuration = model.config
|
| 90 |
+
```"""
|
| 91 |
+
|
| 92 |
+
model_type = "new"
|
| 93 |
+
|
| 94 |
+
def __init__(
|
| 95 |
+
self,
|
| 96 |
+
vocab_size=30528,
|
| 97 |
+
hidden_size=768,
|
| 98 |
+
num_hidden_layers=12,
|
| 99 |
+
num_attention_heads=12,
|
| 100 |
+
intermediate_size=3072,
|
| 101 |
+
hidden_act="gelu",
|
| 102 |
+
hidden_dropout_prob=0.1,
|
| 103 |
+
attention_probs_dropout_prob=0.0,
|
| 104 |
+
max_position_embeddings=2048,
|
| 105 |
+
type_vocab_size=1,
|
| 106 |
+
initializer_range=0.02,
|
| 107 |
+
layer_norm_type='layer_norm',
|
| 108 |
+
layer_norm_eps=1e-12,
|
| 109 |
+
# pad_token_id=0,
|
| 110 |
+
position_embedding_type="rope",
|
| 111 |
+
rope_theta=10000.0,
|
| 112 |
+
rope_scaling=None,
|
| 113 |
+
classifier_dropout=None,
|
| 114 |
+
pack_qkv=True,
|
| 115 |
+
unpad_inputs=False,
|
| 116 |
+
use_memory_efficient_attention=False,
|
| 117 |
+
logn_attention_scale=False,
|
| 118 |
+
logn_attention_clip1=False,
|
| 119 |
+
**kwargs,
|
| 120 |
+
):
|
| 121 |
+
super().__init__(**kwargs)
|
| 122 |
+
|
| 123 |
+
self.vocab_size = vocab_size
|
| 124 |
+
self.hidden_size = hidden_size
|
| 125 |
+
self.num_hidden_layers = num_hidden_layers
|
| 126 |
+
self.num_attention_heads = num_attention_heads
|
| 127 |
+
self.hidden_act = hidden_act
|
| 128 |
+
self.intermediate_size = intermediate_size
|
| 129 |
+
self.hidden_dropout_prob = hidden_dropout_prob
|
| 130 |
+
self.attention_probs_dropout_prob = attention_probs_dropout_prob
|
| 131 |
+
self.max_position_embeddings = max_position_embeddings
|
| 132 |
+
self.type_vocab_size = type_vocab_size
|
| 133 |
+
self.initializer_range = initializer_range
|
| 134 |
+
self.layer_norm_type = layer_norm_type
|
| 135 |
+
self.layer_norm_eps = layer_norm_eps
|
| 136 |
+
self.position_embedding_type = position_embedding_type
|
| 137 |
+
self.rope_theta = rope_theta
|
| 138 |
+
self.rope_scaling = rope_scaling
|
| 139 |
+
self.classifier_dropout = classifier_dropout
|
| 140 |
+
|
| 141 |
+
self.pack_qkv = pack_qkv
|
| 142 |
+
self.unpad_inputs = unpad_inputs
|
| 143 |
+
self.use_memory_efficient_attention = use_memory_efficient_attention
|
| 144 |
+
self.logn_attention_scale = logn_attention_scale
|
| 145 |
+
self.logn_attention_clip1 = logn_attention_clip1
|
modules.json
ADDED
|
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[
|
| 2 |
+
{
|
| 3 |
+
"idx": 0,
|
| 4 |
+
"name": "0",
|
| 5 |
+
"path": "",
|
| 6 |
+
"type": "sentence_transformers.models.Transformer"
|
| 7 |
+
},
|
| 8 |
+
{
|
| 9 |
+
"idx": 1,
|
| 10 |
+
"name": "1",
|
| 11 |
+
"path": "1_Pooling",
|
| 12 |
+
"type": "sentence_transformers.models.Pooling"
|
| 13 |
+
}
|
| 14 |
+
]
|
sentence_bert_config.json
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"max_seq_length": 8192,
|
| 3 |
+
"do_lower_case": false
|
| 4 |
+
}
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"cls_token": {
|
| 3 |
+
"content": "[CLS]",
|
| 4 |
+
"lstrip": false,
|
| 5 |
+
"normalized": false,
|
| 6 |
+
"rstrip": false,
|
| 7 |
+
"single_word": false
|
| 8 |
+
},
|
| 9 |
+
"mask_token": {
|
| 10 |
+
"content": "[MASK]",
|
| 11 |
+
"lstrip": false,
|
| 12 |
+
"normalized": false,
|
| 13 |
+
"rstrip": false,
|
| 14 |
+
"single_word": false
|
| 15 |
+
},
|
| 16 |
+
"pad_token": {
|
| 17 |
+
"content": "[PAD]",
|
| 18 |
+
"lstrip": false,
|
| 19 |
+
"normalized": false,
|
| 20 |
+
"rstrip": false,
|
| 21 |
+
"single_word": false
|
| 22 |
+
},
|
| 23 |
+
"sep_token": {
|
| 24 |
+
"content": "[SEP]",
|
| 25 |
+
"lstrip": false,
|
| 26 |
+
"normalized": false,
|
| 27 |
+
"rstrip": false,
|
| 28 |
+
"single_word": false
|
| 29 |
+
},
|
| 30 |
+
"unk_token": {
|
| 31 |
+
"content": "[UNK]",
|
| 32 |
+
"lstrip": false,
|
| 33 |
+
"normalized": false,
|
| 34 |
+
"rstrip": false,
|
| 35 |
+
"single_word": false
|
| 36 |
+
}
|
| 37 |
+
}
|
tokenizer.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,62 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"added_tokens_decoder": {
|
| 3 |
+
"0": {
|
| 4 |
+
"content": "[PAD]",
|
| 5 |
+
"lstrip": false,
|
| 6 |
+
"normalized": false,
|
| 7 |
+
"rstrip": false,
|
| 8 |
+
"single_word": false,
|
| 9 |
+
"special": true
|
| 10 |
+
},
|
| 11 |
+
"100": {
|
| 12 |
+
"content": "[UNK]",
|
| 13 |
+
"lstrip": false,
|
| 14 |
+
"normalized": false,
|
| 15 |
+
"rstrip": false,
|
| 16 |
+
"single_word": false,
|
| 17 |
+
"special": true
|
| 18 |
+
},
|
| 19 |
+
"101": {
|
| 20 |
+
"content": "[CLS]",
|
| 21 |
+
"lstrip": false,
|
| 22 |
+
"normalized": false,
|
| 23 |
+
"rstrip": false,
|
| 24 |
+
"single_word": false,
|
| 25 |
+
"special": true
|
| 26 |
+
},
|
| 27 |
+
"102": {
|
| 28 |
+
"content": "[SEP]",
|
| 29 |
+
"lstrip": false,
|
| 30 |
+
"normalized": false,
|
| 31 |
+
"rstrip": false,
|
| 32 |
+
"single_word": false,
|
| 33 |
+
"special": true
|
| 34 |
+
},
|
| 35 |
+
"103": {
|
| 36 |
+
"content": "[MASK]",
|
| 37 |
+
"lstrip": false,
|
| 38 |
+
"normalized": false,
|
| 39 |
+
"rstrip": false,
|
| 40 |
+
"single_word": false,
|
| 41 |
+
"special": true
|
| 42 |
+
}
|
| 43 |
+
},
|
| 44 |
+
"clean_up_tokenization_spaces": true,
|
| 45 |
+
"cls_token": "[CLS]",
|
| 46 |
+
"do_lower_case": true,
|
| 47 |
+
"mask_token": "[MASK]",
|
| 48 |
+
"max_length": 512,
|
| 49 |
+
"model_max_length": 8192,
|
| 50 |
+
"pad_to_multiple_of": null,
|
| 51 |
+
"pad_token": "[PAD]",
|
| 52 |
+
"pad_token_type_id": 0,
|
| 53 |
+
"padding_side": "right",
|
| 54 |
+
"sep_token": "[SEP]",
|
| 55 |
+
"stride": 0,
|
| 56 |
+
"strip_accents": null,
|
| 57 |
+
"tokenize_chinese_chars": true,
|
| 58 |
+
"tokenizer_class": "BertTokenizer",
|
| 59 |
+
"truncation_side": "right",
|
| 60 |
+
"truncation_strategy": "longest_first",
|
| 61 |
+
"unk_token": "[UNK]"
|
| 62 |
+
}
|
vocab.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|