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---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:6300
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: intfloat/e5-large-unsupervised
widget:
- source_sentence: What are the key components of the transparency provisions included
in the Consolidated Appropriations Act of 2021 regarding healthcare?
sentences:
- The report includes information on legal proceedings under 'Note 13 — Commitments
and Contingencies — Litigation and Other Legal Matters' which is a part of the
consolidated financial statements
- The Consolidated Appropriations Act of 2021 was signed into law in December 2020
and contains further transparency provisions requiring group health plans and
health insurance issuers to report certain prescription drug costs, overall spending
on health services and prescription drugs, and information about premiums and
the impact of rebates and other remuneration on premiums and out-of-pocket costs
to the Tri-Departments.
- In 2023, the company recorded other operating charges of $1,951 million.
- source_sentence: What technology does the Tax Advisor use and for what purpose in
Intuit's offerings?
sentences:
- In 2023, Goldman Sachs' investments in funds at NAV primarily included firm-sponsored
private equity, credit, real estate, and hedge funds. These funds are involved
in various types of investments such as leveraged buyouts, recapitalizations,
growth investments, and distressed investments for private equity, while credit
funds are focused on providing private high-yield capital for leveraged and management
buyout transactions. Real estate funds invest globally in real estate assets,
and hedge funds adopt a fundamental bottom-up investment approach.
- Using AI technologies, our Tax Advisor offering leverages information generated
from our ProConnect Tax Online and Lacerte offerings to enable year-round tax
planning services and communicate tax savings strategies to clients.
- '''Note 13 — Commitments and Contingencies'' provides details about litigation
and other legal matters in an Annual Report on Form 10-K.'
- source_sentence: What was the net revenue for the Data Center segment in 2023?
sentences:
- Data Center net revenue of $6.5 billion in 2023 increased by 7%, compared to net
revenue of $6.0 billion in 2022.
- Under its Class 2 insurance license, Caterpillar Insurance Co. Ltd. insures its
parent and affiliates for general liability, property, auto liability and cargo.
It also provides reinsurance to CaterThe pillar Insurance Company under a quota
share reinsurance agreement for its contractual liability and contractors’ equipment
programs in the United States.
- Schwab’s funding of these remaining commitments is dependent upon the occurrence
of certain conditions, and Schwab expects to pay substantially all of these commitments
between 2024 and 2027.
- source_sentence: What are the three principles of liquidity risk management at Goldman
Sachs?
sentences:
- The Company determines if an arrangement is a lease at inception and classifies
its leases at commencement. Operating leases are included in operating lease right-of-use
("ROU") assets and current and noncurrent operating lease liabilities on the Company’s
consolidated balance sheets.
- Garmin Ltd. reported a net income of $1,289,636 for the fiscal year ended December
30, 2023.
- 'Goldman Sachs manages liquidity risk based on three principles: 1) hold sufficient
excess liquidity in the form of GCLA to cover outflows during a stressed period,
2) maintain appropriate Asset-Liability Management, and 3) maintain a viable Contingency
Funding Plan.'
- source_sentence: What was the total cost and expenses reported by Berkshire Hathaway
for the year ended December 31, 2023?
sentences:
- Total costs and expenses | | 321,144 | | | 266,484 | | | 243,752
- Qulipta (atogepant) is a calcitonin gene-related peptide receptor antagonist indicated
for the preventive treatment of episodic and chronic migraine in adults. Qulipta
is commercialized in the United States and Canada and is approved in the European
Union under the brand name Aquipta.
- Item 3 'Legal Proceedings' is integrated by reference to other parts including
Note 22 — 'Environmental and legal matters' and Part II, Item 8.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: E5 unsupervised Financial Matryoshka
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.7271428571428571
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.85
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8785714285714286
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9114285714285715
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7271428571428571
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2833333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17571428571428568
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09114285714285714
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7271428571428571
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.85
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8785714285714286
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9114285714285715
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.822517236613446
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7936921768707483
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7973883589026711
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.7271428571428571
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8457142857142858
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.88
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9128571428571428
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7271428571428571
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.28190476190476194
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.176
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09128571428571429
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7271428571428571
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8457142857142858
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.88
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9128571428571428
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8223709830528422
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.793145691609977
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7966990460475021
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.72
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8457142857142858
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8714285714285714
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9057142857142857
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.72
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.28190476190476194
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17428571428571424
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09057142857142855
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.72
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8457142857142858
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8714285714285714
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9057142857142857
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8159991941699124
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7869370748299319
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7906967878713818
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.7085714285714285
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8285714285714286
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8728571428571429
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8985714285714286
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7085714285714285
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2761904761904762
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17457142857142854
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08985714285714284
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7085714285714285
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8285714285714286
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8728571428571429
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8985714285714286
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8073517667504667
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7777108843537414
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7815591417851651
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.6757142857142857
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8185714285714286
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8457142857142858
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8842857142857142
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6757142857142857
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27285714285714285
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16914285714285712
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08842857142857141
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6757142857142857
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8185714285714286
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8457142857142858
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8842857142857142
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7861731335824387
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7542681405895693
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7588497811523153
name: Cosine Map@100
---
# E5 unsupervised Financial Matryoshka
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/e5-large-unsupervised](https://huggingface.co/intfloat/e5-large-unsupervised) on the json dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [intfloat/e5-large-unsupervised](https://huggingface.co/intfloat/e5-large-unsupervised) <!-- at revision 15af9288f69a6291f37bfb89b47e71abc747b206 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- json
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("schawla2/e5-unsupervised-financial-matryoshka")
# Run inference
sentences = [
'What was the total cost and expenses reported by Berkshire Hathaway for the year ended December 31, 2023?',
'Total costs and expenses | | 321,144 | | | 266,484 | | | 243,752',
'Qulipta (atogepant) is a calcitonin gene-related peptide receptor antagonist indicated for the preventive treatment of episodic and chronic migraine in adults. Qulipta is commercialized in the United States and Canada and is approved in the European Union under the brand name Aquipta.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 |
|:--------------------|:-----------|:-----------|:----------|:-----------|:-----------|
| cosine_accuracy@1 | 0.7271 | 0.7271 | 0.72 | 0.7086 | 0.6757 |
| cosine_accuracy@3 | 0.85 | 0.8457 | 0.8457 | 0.8286 | 0.8186 |
| cosine_accuracy@5 | 0.8786 | 0.88 | 0.8714 | 0.8729 | 0.8457 |
| cosine_accuracy@10 | 0.9114 | 0.9129 | 0.9057 | 0.8986 | 0.8843 |
| cosine_precision@1 | 0.7271 | 0.7271 | 0.72 | 0.7086 | 0.6757 |
| cosine_precision@3 | 0.2833 | 0.2819 | 0.2819 | 0.2762 | 0.2729 |
| cosine_precision@5 | 0.1757 | 0.176 | 0.1743 | 0.1746 | 0.1691 |
| cosine_precision@10 | 0.0911 | 0.0913 | 0.0906 | 0.0899 | 0.0884 |
| cosine_recall@1 | 0.7271 | 0.7271 | 0.72 | 0.7086 | 0.6757 |
| cosine_recall@3 | 0.85 | 0.8457 | 0.8457 | 0.8286 | 0.8186 |
| cosine_recall@5 | 0.8786 | 0.88 | 0.8714 | 0.8729 | 0.8457 |
| cosine_recall@10 | 0.9114 | 0.9129 | 0.9057 | 0.8986 | 0.8843 |
| **cosine_ndcg@10** | **0.8225** | **0.8224** | **0.816** | **0.8074** | **0.7862** |
| cosine_mrr@10 | 0.7937 | 0.7931 | 0.7869 | 0.7777 | 0.7543 |
| cosine_map@100 | 0.7974 | 0.7967 | 0.7907 | 0.7816 | 0.7588 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### json
* Dataset: json
* Size: 6,300 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 20.8 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 45.24 tokens</li><li>max: 326 tokens</li></ul> |
* Samples:
| anchor | positive |
|:--------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>How many full-time employees did Microsoft report as of June 30, 2023?</code> | <code>As of June 30, 2023, we employed approximately 221,000 people on a full-time basis, 120,000 in the U.S. and 101,000 internationally.</code> |
| <code>What was the total amount CSC paid for Series G preferred stock repurchases in 2023?</code> | <code>In 2023, CSC repurchased 42,036 depositary shares representing interests in Series G preferred stock for a total amount of $42 million.</code> |
| <code>What does Note 13 in the Annual Report on Form 10-K discuss?</code> | <code>For a discussion of legal and other proceedings in which we are involved, see Note 13 - Commitments and Contingencies in the Notes to Consolidated Financial Statements.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
|:---------:|:-------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.2030 | 10 | 9.3166 | - | - | - | - | - |
| 0.4061 | 20 | 3.7163 | - | - | - | - | - |
| 0.6091 | 30 | 2.8216 | - | - | - | - | - |
| 0.8122 | 40 | 1.9313 | - | - | - | - | - |
| 1.0 | 50 | 1.5613 | 0.8230 | 0.8237 | 0.8153 | 0.8036 | 0.7771 |
| 1.2030 | 60 | 1.0926 | - | - | - | - | - |
| 1.4061 | 70 | 0.3367 | - | - | - | - | - |
| 1.6091 | 80 | 0.3958 | - | - | - | - | - |
| 1.8122 | 90 | 0.6527 | - | - | - | - | - |
| 2.0 | 100 | 0.4483 | 0.8202 | 0.8209 | 0.8118 | 0.8033 | 0.7792 |
| 2.2030 | 110 | 0.1823 | - | - | - | - | - |
| 2.4061 | 120 | 0.0494 | - | - | - | - | - |
| 2.6091 | 130 | 0.1204 | - | - | - | - | - |
| 2.8122 | 140 | 0.2021 | - | - | - | - | - |
| 3.0 | 150 | 0.2088 | 0.8211 | 0.8213 | 0.8148 | 0.8064 | 0.7825 |
| 3.2030 | 160 | 0.062 | - | - | - | - | - |
| 3.4061 | 170 | 0.022 | - | - | - | - | - |
| 3.6091 | 180 | 0.0654 | - | - | - | - | - |
| 3.8122 | 190 | 0.1481 | - | - | - | - | - |
| **3.934** | **196** | **-** | **0.8225** | **0.8224** | **0.816** | **0.8074** | **0.7862** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.16
- Sentence Transformers: 3.3.1
- Transformers: 4.48.1
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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