Vignesh finetuned bge2
This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5 on the json dataset. It maps sentences & paragraphs to a 768-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: BAAI/bge-base-en-v1.5
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 768 tokens
- Similarity Function: Cosine Similarity
- Training Dataset:
- json
- Language: en
- License: apache-2.0
Model Sources
- Documentation: Sentence Transformers Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Sentence Transformers on Hugging Face
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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:
pip install -U sentence-transformers
Then you can load this model and run inference.
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("viggypoker1/Vignesh-finetuned-bge2")
# Run inference
sentences = [
"What does the term 'Acquired brands' refer to and how does it affect the reported volumes?",
"'Acquired brands' refers to brands acquired during the past 12 months. Typically, the Company has not reported unit case volume or recognized concentrate sales volume related to acquired brands in periods prior to the closing of a transaction. Therefore, the unit case volume and concentrate sales volume related to an acquired brand are incremental to prior year volume.",
'The Company made matching contributions to employee accounts in connection with the 401(k) plan of $37.3 million in fiscal 2023, $37.9 million in fiscal 2022 and $34.1 million in fiscal 2021.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Information Retrieval
- Dataset:
dim_768
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.7 |
cosine_accuracy@3 | 0.8414 |
cosine_accuracy@5 | 0.8786 |
cosine_accuracy@10 | 0.92 |
cosine_precision@1 | 0.7 |
cosine_precision@3 | 0.2805 |
cosine_precision@5 | 0.1757 |
cosine_precision@10 | 0.092 |
cosine_recall@1 | 0.7 |
cosine_recall@3 | 0.8414 |
cosine_recall@5 | 0.8786 |
cosine_recall@10 | 0.92 |
cosine_ndcg@10 | 0.813 |
cosine_mrr@10 | 0.7784 |
cosine_map@100 | 0.7817 |
Information Retrieval
- Dataset:
dim_512
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.6914 |
cosine_accuracy@3 | 0.84 |
cosine_accuracy@5 | 0.8857 |
cosine_accuracy@10 | 0.9243 |
cosine_precision@1 | 0.6914 |
cosine_precision@3 | 0.28 |
cosine_precision@5 | 0.1771 |
cosine_precision@10 | 0.0924 |
cosine_recall@1 | 0.6914 |
cosine_recall@3 | 0.84 |
cosine_recall@5 | 0.8857 |
cosine_recall@10 | 0.9243 |
cosine_ndcg@10 | 0.8121 |
cosine_mrr@10 | 0.7758 |
cosine_map@100 | 0.7787 |
Information Retrieval
- Dataset:
dim_256
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.69 |
cosine_accuracy@3 | 0.8286 |
cosine_accuracy@5 | 0.8729 |
cosine_accuracy@10 | 0.9143 |
cosine_precision@1 | 0.69 |
cosine_precision@3 | 0.2762 |
cosine_precision@5 | 0.1746 |
cosine_precision@10 | 0.0914 |
cosine_recall@1 | 0.69 |
cosine_recall@3 | 0.8286 |
cosine_recall@5 | 0.8729 |
cosine_recall@10 | 0.9143 |
cosine_ndcg@10 | 0.8041 |
cosine_mrr@10 | 0.7685 |
cosine_map@100 | 0.772 |
Information Retrieval
- Dataset:
dim_128
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.67 |
cosine_accuracy@3 | 0.8171 |
cosine_accuracy@5 | 0.8657 |
cosine_accuracy@10 | 0.9071 |
cosine_precision@1 | 0.67 |
cosine_precision@3 | 0.2724 |
cosine_precision@5 | 0.1731 |
cosine_precision@10 | 0.0907 |
cosine_recall@1 | 0.67 |
cosine_recall@3 | 0.8171 |
cosine_recall@5 | 0.8657 |
cosine_recall@10 | 0.9071 |
cosine_ndcg@10 | 0.7905 |
cosine_mrr@10 | 0.7529 |
cosine_map@100 | 0.7567 |
Information Retrieval
- Dataset:
dim_64
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.6314 |
cosine_accuracy@3 | 0.7943 |
cosine_accuracy@5 | 0.8386 |
cosine_accuracy@10 | 0.8829 |
cosine_precision@1 | 0.6314 |
cosine_precision@3 | 0.2648 |
cosine_precision@5 | 0.1677 |
cosine_precision@10 | 0.0883 |
cosine_recall@1 | 0.6314 |
cosine_recall@3 | 0.7943 |
cosine_recall@5 | 0.8386 |
cosine_recall@10 | 0.8829 |
cosine_ndcg@10 | 0.7591 |
cosine_mrr@10 | 0.7192 |
cosine_map@100 | 0.7236 |
Training Details
Training Dataset
json
- Dataset: json
- Size: 311,351 training samples
- Columns:
anchor
andpositive
- Approximate statistics based on the first 1000 samples:
anchor positive type string string details - min: 9 tokens
- mean: 20.47 tokens
- max: 41 tokens
- min: 7 tokens
- mean: 46.65 tokens
- max: 512 tokens
- Samples:
anchor positive What section from item 8 addresses financial information?
Item 8 covers 'Financial Statements and Supplementary Data' relating to financial information.
What was the percentage increase in interest income from 2022 to 2023?
Interest income increased $769 million, or 259%, in the year ended December 31, 2023 as compared to the year ended December 31, 2022. This increase was primarily due to higher interest earned on our cash and cash equivalents and short-term investments in the year ended December 31, 2023 as compared to the prior year due to rising interest rates and our increasing portfolio balance.
What was the operating margin for UnitedHealthcare in 2023?
The operating margin for UnitedHealthcare in 2023 was reported as 5.8%.
- Loss:
MatryoshkaLoss
with these parameters:{ "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 }
Evaluation Dataset
json
- Dataset: json
- Size: 700 evaluation samples
- Columns:
anchor
andpositive
- Approximate statistics based on the first 700 samples:
anchor positive type string string details - min: 7 tokens
- mean: 20.59 tokens
- max: 40 tokens
- min: 6 tokens
- mean: 47.59 tokens
- max: 326 tokens
- Samples:
anchor positive What was the maximum borrowing capacity available from the Federal Home Loan Bank of Boston as of December 31, 2023?
The maximum borrowing capacity available from the FHLBB as of December 31, 2023 was approximately $1.0 billion.
What new compliance requirement was established by the CFPB's final rule issued on March 30, 2023, regarding small business credit applications?
On March 30, 2023, the CFPB adopted a final rule requiring covered financial institutions, such as us, to collect and report data to the CFPB regarding certain small business credit applications.
What potential impact could continued geopolitical tensions have on the business?
While the ongoing Russia-Ukraine and Israel conflicts are still evolving and outcomes remain uncertain, the business does not expect the resulting challenging macroeconomic conditions to have a material impact currently. However, if conflicts continue or worsen, it could lead to greater disruptions and uncertainty, negatively impacting the business.
- Loss:
MatryoshkaLoss
with these parameters:{ "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
: epochper_device_train_batch_size
: 128per_device_eval_batch_size
: 16gradient_accumulation_steps
: 16learning_rate
: 2e-05num_train_epochs
: 10lr_scheduler_type
: cosinewarmup_ratio
: 0.1fp16
: Truetf32
: Falseload_best_model_at_end
: Trueoptim
: adamw_torch_fusedbatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: epochprediction_loss_only
: Trueper_device_train_batch_size
: 128per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 16eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 2e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 10max_steps
: -1lr_scheduler_type
: cosinelr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Truefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Falselocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Trueignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torch_fusedoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseuse_liger_kernel
: Falseeval_use_gather_object
: Falsebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Click to expand
Epoch | Step | Training Loss | loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
---|---|---|---|---|---|---|---|---|
0.0658 | 10 | 12.7958 | - | - | - | - | - | - |
0.1315 | 20 | 16.8225 | - | - | - | - | - | - |
0.1973 | 30 | 20.1236 | - | - | - | - | - | - |
0.2630 | 40 | 22.0845 | - | - | - | - | - | - |
0.3288 | 50 | 19.7865 | - | - | - | - | - | - |
0.3946 | 60 | 6.0102 | - | - | - | - | - | - |
0.4603 | 70 | 3.7813 | - | - | - | - | - | - |
0.5261 | 80 | 2.8675 | - | - | - | - | - | - |
0.5919 | 90 | 2.2002 | - | - | - | - | - | - |
0.6576 | 100 | 1.8334 | - | - | - | - | - | - |
0.7234 | 110 | 1.5052 | - | - | - | - | - | - |
0.7891 | 120 | 1.3454 | - | - | - | - | - | - |
0.8549 | 130 | 1.2089 | - | - | - | - | - | - |
0.9207 | 140 | 1.0615 | - | - | - | - | - | - |
0.9864 | 150 | 1.011 | - | - | - | - | - | - |
0.9996 | 152 | - | 0.2963 | 0.7043 | 0.7228 | 0.7462 | 0.6496 | 0.7566 |
1.0522 | 160 | 7.9844 | - | - | - | - | - | - |
1.1180 | 170 | 12.726 | - | - | - | - | - | - |
1.1837 | 180 | 17.3762 | - | - | - | - | - | - |
1.2495 | 190 | 19.358 | - | - | - | - | - | - |
1.3152 | 200 | 19.4805 | - | - | - | - | - | - |
1.3810 | 210 | 5.7452 | - | - | - | - | - | - |
1.4468 | 220 | 1.3857 | - | - | - | - | - | - |
1.5125 | 230 | 0.9792 | - | - | - | - | - | - |
1.5783 | 240 | 0.8632 | - | - | - | - | - | - |
1.6441 | 250 | 0.8256 | - | - | - | - | - | - |
1.7098 | 260 | 0.742 | - | - | - | - | - | - |
1.7756 | 270 | 0.7307 | - | - | - | - | - | - |
1.8413 | 280 | 0.7064 | - | - | - | - | - | - |
1.9071 | 290 | 0.6492 | - | - | - | - | - | - |
1.9729 | 300 | 0.6265 | - | - | - | - | - | - |
1.9992 | 304 | - | 0.2345 | 0.7145 | 0.7317 | 0.7548 | 0.6706 | 0.7609 |
2.0386 | 310 | 4.0854 | - | - | - | - | - | - |
2.1044 | 320 | 11.4485 | - | - | - | - | - | - |
2.1702 | 330 | 14.1851 | - | - | - | - | - | - |
2.2359 | 340 | 17.7422 | - | - | - | - | - | - |
2.3017 | 350 | 19.2742 | - | - | - | - | - | - |
2.3674 | 360 | 7.3918 | - | - | - | - | - | - |
2.4332 | 370 | 1.0444 | - | - | - | - | - | - |
2.4990 | 380 | 0.6947 | - | - | - | - | - | - |
2.5647 | 390 | 0.6 | - | - | - | - | - | - |
2.6305 | 400 | 0.6005 | - | - | - | - | - | - |
2.6963 | 410 | 0.5314 | - | - | - | - | - | - |
2.7620 | 420 | 0.5238 | - | - | - | - | - | - |
2.8278 | 430 | 0.5207 | - | - | - | - | - | - |
2.8935 | 440 | 0.5075 | - | - | - | - | - | - |
2.9593 | 450 | 0.4673 | - | - | - | - | - | - |
2.9988 | 456 | - | 0.2111 | 0.7252 | 0.7333 | 0.7530 | 0.6821 | 0.7617 |
3.0251 | 460 | 1.5162 | - | - | - | - | - | - |
3.0908 | 470 | 10.5824 | - | - | - | - | - | - |
3.1566 | 480 | 11.8184 | - | - | - | - | - | - |
3.2224 | 490 | 16.3944 | - | - | - | - | - | - |
3.2881 | 500 | 18.1591 | - | - | - | - | - | - |
3.3539 | 510 | 10.8653 | - | - | - | - | - | - |
3.4196 | 520 | 0.8936 | - | - | - | - | - | - |
3.4854 | 530 | 0.5606 | - | - | - | - | - | - |
3.5512 | 540 | 0.4724 | - | - | - | - | - | - |
3.6169 | 550 | 0.4681 | - | - | - | - | - | - |
3.6827 | 560 | 0.4334 | - | - | - | - | - | - |
3.7485 | 570 | 0.4005 | - | - | - | - | - | - |
3.8142 | 580 | 0.4224 | - | - | - | - | - | - |
3.8800 | 590 | 0.4296 | - | - | - | - | - | - |
3.9457 | 600 | 0.3788 | - | - | - | - | - | - |
3.9984 | 608 | - | 0.1889 | 0.7345 | 0.7469 | 0.7647 | 0.6906 | 0.7633 |
4.0115 | 610 | 0.5548 | - | - | - | - | - | - |
4.0773 | 620 | 8.6803 | - | - | - | - | - | - |
4.1430 | 630 | 10.6235 | - | - | - | - | - | - |
4.2088 | 640 | 14.5689 | - | - | - | - | - | - |
4.2746 | 650 | 17.649 | - | - | - | - | - | - |
4.3403 | 660 | 13.9682 | - | - | - | - | - | - |
4.4061 | 670 | 0.7801 | - | - | - | - | - | - |
4.4718 | 680 | 0.4848 | - | - | - | - | - | - |
4.5376 | 690 | 0.4082 | - | - | - | - | - | - |
4.6034 | 700 | 0.3883 | - | - | - | - | - | - |
4.6691 | 710 | 0.3737 | - | - | - | - | - | - |
4.7349 | 720 | 0.3485 | - | - | - | - | - | - |
4.8007 | 730 | 0.3547 | - | - | - | - | - | - |
4.8664 | 740 | 0.357 | - | - | - | - | - | - |
4.9322 | 750 | 0.3223 | - | - | - | - | - | - |
4.9979 | 760 | 0.3322 | 0.1843 | 0.7364 | 0.7482 | 0.7645 | 0.6911 | 0.7652 |
5.0637 | 770 | 6.5343 | - | - | - | - | - | - |
5.1295 | 780 | 10.1093 | - | - | - | - | - | - |
5.1952 | 790 | 13.3253 | - | - | - | - | - | - |
5.2610 | 800 | 16.6724 | - | - | - | - | - | - |
5.3268 | 810 | 15.6655 | - | - | - | - | - | - |
5.3925 | 820 | 2.0319 | - | - | - | - | - | - |
5.4583 | 830 | 0.4315 | - | - | - | - | - | - |
5.5240 | 840 | 0.3544 | - | - | - | - | - | - |
5.5898 | 850 | 0.3488 | - | - | - | - | - | - |
5.6556 | 860 | 0.3301 | - | - | - | - | - | - |
5.7213 | 870 | 0.3035 | - | - | - | - | - | - |
5.7871 | 880 | 0.3123 | - | - | - | - | - | - |
5.8529 | 890 | 0.3149 | - | - | - | - | - | - |
5.9186 | 900 | 0.2857 | - | - | - | - | - | - |
5.9844 | 910 | 0.3021 | - | - | - | - | - | - |
5.9975 | 912 | - | 0.1704 | 0.7442 | 0.7527 | 0.7643 | 0.7031 | 0.7700 |
6.0501 | 920 | 4.5418 | - | - | - | - | - | - |
6.1159 | 930 | 8.909 | - | - | - | - | - | - |
6.1817 | 940 | 12.7023 | - | - | - | - | - | - |
6.2474 | 950 | 15.6328 | - | - | - | - | - | - |
6.3132 | 960 | 17.1026 | - | - | - | - | - | - |
6.3790 | 970 | 3.8174 | - | - | - | - | - | - |
6.4447 | 980 | 0.4035 | - | - | - | - | - | - |
6.5105 | 990 | 0.3281 | - | - | - | - | - | - |
6.5762 | 1000 | 0.3126 | - | - | - | - | - | - |
6.6420 | 1010 | 0.304 | - | - | - | - | - | - |
6.7078 | 1020 | 0.2692 | - | - | - | - | - | - |
6.7735 | 1030 | 0.2807 | - | - | - | - | - | - |
6.8393 | 1040 | 0.2993 | - | - | - | - | - | - |
6.9051 | 1050 | 0.2721 | - | - | - | - | - | - |
6.9708 | 1060 | 0.2674 | - | - | - | - | - | - |
6.9971 | 1064 | - | 0.1596 | 0.7481 | 0.7607 | 0.7723 | 0.7074 | 0.7735 |
7.0366 | 1070 | 2.5499 | - | - | - | - | - | - |
7.1023 | 1080 | 8.8274 | - | - | - | - | - | - |
7.1681 | 1090 | 11.3224 | - | - | - | - | - | - |
7.2339 | 1100 | 15.0825 | - | - | - | - | - | - |
7.2996 | 1110 | 17.6647 | - | - | - | - | - | - |
7.3654 | 1120 | 6.0271 | - | - | - | - | - | - |
7.4312 | 1130 | 0.3838 | - | - | - | - | - | - |
7.4969 | 1140 | 0.3137 | - | - | - | - | - | - |
7.5627 | 1150 | 0.285 | - | - | - | - | - | - |
7.6284 | 1160 | 0.2913 | - | - | - | - | - | - |
7.6942 | 1170 | 0.268 | - | - | - | - | - | - |
7.7600 | 1180 | 0.2643 | - | - | - | - | - | - |
7.8257 | 1190 | 0.2702 | - | - | - | - | - | - |
7.8915 | 1200 | 0.2775 | - | - | - | - | - | - |
7.9573 | 1210 | 0.2563 | - | - | - | - | - | - |
7.9967 | 1216 | - | 0.1543 | 0.7495 | 0.7645 | 0.7715 | 0.7124 | 0.7802 |
8.0230 | 1220 | 0.7657 | - | - | - | - | - | - |
8.0888 | 1230 | 8.542 | - | - | - | - | - | - |
8.1545 | 1240 | 9.9807 | - | - | - | - | - | - |
8.2203 | 1250 | 14.3646 | - | - | - | - | - | - |
8.2861 | 1260 | 16.877 | - | - | - | - | - | - |
8.3518 | 1270 | 10.2992 | - | - | - | - | - | - |
8.4176 | 1280 | 0.363 | - | - | - | - | - | - |
8.4834 | 1290 | 0.304 | - | - | - | - | - | - |
8.5491 | 1300 | 0.2851 | - | - | - | - | - | - |
8.6149 | 1310 | 0.2853 | - | - | - | - | - | - |
8.6806 | 1320 | 0.2676 | - | - | - | - | - | - |
8.7464 | 1330 | 0.2522 | - | - | - | - | - | - |
8.8122 | 1340 | 0.2619 | - | - | - | - | - | - |
8.8779 | 1350 | 0.2757 | - | - | - | - | - | - |
8.9437 | 1360 | 0.2528 | - | - | - | - | - | - |
8.9963 | 1368 | - | 0.1483 | 0.7529 | 0.7680 | 0.7759 | 0.7172 | 0.7807 |
9.0095 | 1370 | 0.3564 | - | - | - | - | - | - |
9.0752 | 1380 | 7.1402 | - | - | - | - | - | - |
9.1410 | 1390 | 9.4364 | - | - | - | - | - | - |
9.2067 | 1400 | 13.1391 | - | - | - | - | - | - |
9.2725 | 1410 | 16.7827 | - | - | - | - | - | - |
9.3383 | 1420 | 13.456 | - | - | - | - | - | - |
9.4040 | 1430 | 0.5238 | - | - | - | - | - | - |
9.4698 | 1440 | 0.3073 | - | - | - | - | - | - |
9.5356 | 1450 | 0.2773 | - | - | - | - | - | - |
9.6013 | 1460 | 0.2783 | - | - | - | - | - | - |
9.6671 | 1470 | 0.2645 | - | - | - | - | - | - |
9.7328 | 1480 | 0.2495 | - | - | - | - | - | - |
9.7986 | 1490 | 0.2649 | - | - | - | - | - | - |
9.8644 | 1500 | 0.2655 | - | - | - | - | - | - |
9.9301 | 1510 | 0.2395 | - | - | - | - | - | - |
9.9959 | 1520 | 0.2569 | 0.1453 | 0.7567 | 0.772 | 0.7787 | 0.7236 | 0.7817 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.1
- Transformers: 4.45.2
- PyTorch: 2.6.0+cu124
- Accelerate: 1.3.0
- Datasets: 2.19.1
- Tokenizers: 0.20.3
Citation
BibTeX
Sentence Transformers
@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
@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
@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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BAAI/bge-base-en-v1.5Evaluation results
- Cosine Accuracy@1 on dim 768self-reported0.700
- Cosine Accuracy@3 on dim 768self-reported0.841
- Cosine Accuracy@5 on dim 768self-reported0.879
- Cosine Accuracy@10 on dim 768self-reported0.920
- Cosine Precision@1 on dim 768self-reported0.700
- Cosine Precision@3 on dim 768self-reported0.280
- Cosine Precision@5 on dim 768self-reported0.176
- Cosine Precision@10 on dim 768self-reported0.092
- Cosine Recall@1 on dim 768self-reported0.700
- Cosine Recall@3 on dim 768self-reported0.841