BGE-M3 Financial Matryoshka
This is a sentence-transformers model finetuned from BAAI/bge-m3. 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: BAAI/bge-m3
- Maximum Sequence Length: 8192 tokens
- Output Dimensionality: 1024 tokens
- Similarity Function: Cosine Similarity
- 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': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, '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("haophancs/bge-m3-financial-matryoshka")
# Run inference
sentences = [
'As of January 28, 2024 the net carrying value of our inventories was $1.3 billion, which included provisions for obsolete and damaged inventory of $139.7 million.',
"What is the status of the company's inventory as of January 28, 2024, in terms of its valuation and provisions for obsolescence?",
'What is the relationship between the ESG goals and the long-term growth strategy?',
]
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]
Evaluation
Metrics
Information Retrieval
- Dataset:
dim_1024
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.7171 |
cosine_accuracy@3 | 0.8314 |
cosine_accuracy@5 | 0.87 |
cosine_accuracy@10 | 0.9143 |
cosine_precision@1 | 0.7171 |
cosine_precision@3 | 0.2771 |
cosine_precision@5 | 0.174 |
cosine_precision@10 | 0.0914 |
cosine_recall@1 | 0.7171 |
cosine_recall@3 | 0.8314 |
cosine_recall@5 | 0.87 |
cosine_recall@10 | 0.9143 |
cosine_ndcg@10 | 0.8152 |
cosine_mrr@10 | 0.7836 |
cosine_map@100 | 0.7867 |
Information Retrieval
- Dataset:
dim_768
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.7129 |
cosine_accuracy@3 | 0.8343 |
cosine_accuracy@5 | 0.8657 |
cosine_accuracy@10 | 0.91 |
cosine_precision@1 | 0.7129 |
cosine_precision@3 | 0.2781 |
cosine_precision@5 | 0.1731 |
cosine_precision@10 | 0.091 |
cosine_recall@1 | 0.7129 |
cosine_recall@3 | 0.8343 |
cosine_recall@5 | 0.8657 |
cosine_recall@10 | 0.91 |
cosine_ndcg@10 | 0.8122 |
cosine_mrr@10 | 0.7809 |
cosine_map@100 | 0.7843 |
Information Retrieval
- Dataset:
dim_512
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.7114 |
cosine_accuracy@3 | 0.8357 |
cosine_accuracy@5 | 0.8643 |
cosine_accuracy@10 | 0.91 |
cosine_precision@1 | 0.7114 |
cosine_precision@3 | 0.2786 |
cosine_precision@5 | 0.1729 |
cosine_precision@10 | 0.091 |
cosine_recall@1 | 0.7114 |
cosine_recall@3 | 0.8357 |
cosine_recall@5 | 0.8643 |
cosine_recall@10 | 0.91 |
cosine_ndcg@10 | 0.811 |
cosine_mrr@10 | 0.7793 |
cosine_map@100 | 0.7827 |
Information Retrieval
- Dataset:
dim_384
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.7143 |
cosine_accuracy@3 | 0.8329 |
cosine_accuracy@5 | 0.8629 |
cosine_accuracy@10 | 0.9129 |
cosine_precision@1 | 0.7143 |
cosine_precision@3 | 0.2776 |
cosine_precision@5 | 0.1726 |
cosine_precision@10 | 0.0913 |
cosine_recall@1 | 0.7143 |
cosine_recall@3 | 0.8329 |
cosine_recall@5 | 0.8629 |
cosine_recall@10 | 0.9129 |
cosine_ndcg@10 | 0.8126 |
cosine_mrr@10 | 0.7806 |
cosine_map@100 | 0.7838 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 6,300 training samples
- Columns:
positive
andanchor
- Approximate statistics based on the first 1000 samples:
positive anchor type string string details - min: 11 tokens
- mean: 51.97 tokens
- max: 1146 tokens
- min: 7 tokens
- mean: 21.63 tokens
- max: 47 tokens
- Samples:
positive anchor From fiscal year 2022 to 2023, the cost of revenue as a percentage of total net revenue decreased by 3 percent.
What was the percentage change in cost of revenue as a percentage of total net revenue from fiscal year 2022 to 2023?
•Operating income increased $321 million, or 2%, to $18.1 billion versus year ago due to the increase in net sales, partially offset by a modest decrease in operating margin.
What factors contributed to the increase in operating income for Procter & Gamble in 2023?
market specific brands including 'Aurrera,' 'Lider,' and 'PhonePe.'
What specific brands does Walmart International market?
- Loss:
MatryoshkaLoss
with these parameters:{ "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 768, 512, 384 ], "matryoshka_weights": [ 1, 1, 1, 1 ], "n_dims_per_step": -1 }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: epochper_device_train_batch_size
: 4per_device_eval_batch_size
: 2gradient_accumulation_steps
: 2learning_rate
: 2e-05num_train_epochs
: 4lr_scheduler_type
: cosinewarmup_ratio
: 0.1bf16
: Truetf32
: Trueload_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
: 4per_device_eval_batch_size
: 2per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 2eval_accumulation_steps
: Nonelearning_rate
: 2e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 4max_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
: Truefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Truelocal_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
: Falsebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Click to expand
Epoch | Step | Training Loss | dim_1024_cosine_map@100 | dim_384_cosine_map@100 | dim_512_cosine_map@100 | dim_768_cosine_map@100 |
---|---|---|---|---|---|---|
0.0127 | 10 | 0.2059 | - | - | - | - |
0.0254 | 20 | 0.2612 | - | - | - | - |
0.0381 | 30 | 0.0873 | - | - | - | - |
0.0508 | 40 | 0.1352 | - | - | - | - |
0.0635 | 50 | 0.156 | - | - | - | - |
0.0762 | 60 | 0.0407 | - | - | - | - |
0.0889 | 70 | 0.09 | - | - | - | - |
0.1016 | 80 | 0.027 | - | - | - | - |
0.1143 | 90 | 0.0978 | - | - | - | - |
0.1270 | 100 | 0.0105 | - | - | - | - |
0.1397 | 110 | 0.0402 | - | - | - | - |
0.1524 | 120 | 0.0745 | - | - | - | - |
0.1651 | 130 | 0.0655 | - | - | - | - |
0.1778 | 140 | 0.0075 | - | - | - | - |
0.1905 | 150 | 0.0141 | - | - | - | - |
0.2032 | 160 | 0.0615 | - | - | - | - |
0.2159 | 170 | 0.0029 | - | - | - | - |
0.2286 | 180 | 0.0269 | - | - | - | - |
0.2413 | 190 | 0.0724 | - | - | - | - |
0.2540 | 200 | 0.0218 | - | - | - | - |
0.2667 | 210 | 0.0027 | - | - | - | - |
0.2794 | 220 | 0.007 | - | - | - | - |
0.2921 | 230 | 0.0814 | - | - | - | - |
0.3048 | 240 | 0.0326 | - | - | - | - |
0.3175 | 250 | 0.0061 | - | - | - | - |
0.3302 | 260 | 0.0471 | - | - | - | - |
0.3429 | 270 | 0.0115 | - | - | - | - |
0.3556 | 280 | 0.0021 | - | - | - | - |
0.3683 | 290 | 0.0975 | - | - | - | - |
0.3810 | 300 | 0.0572 | - | - | - | - |
0.3937 | 310 | 0.0125 | - | - | - | - |
0.4063 | 320 | 0.04 | - | - | - | - |
0.4190 | 330 | 0.0023 | - | - | - | - |
0.4317 | 340 | 0.0121 | - | - | - | - |
0.4444 | 350 | 0.0116 | - | - | - | - |
0.4571 | 360 | 0.0059 | - | - | - | - |
0.4698 | 370 | 0.0217 | - | - | - | - |
0.4825 | 380 | 0.0294 | - | - | - | - |
0.4952 | 390 | 0.1102 | - | - | - | - |
0.5079 | 400 | 0.0103 | - | - | - | - |
0.5206 | 410 | 0.0023 | - | - | - | - |
0.5333 | 420 | 0.0157 | - | - | - | - |
0.5460 | 430 | 0.0805 | - | - | - | - |
0.5587 | 440 | 0.0168 | - | - | - | - |
0.5714 | 450 | 0.1279 | - | - | - | - |
0.5841 | 460 | 0.2012 | - | - | - | - |
0.5968 | 470 | 0.0436 | - | - | - | - |
0.6095 | 480 | 0.0204 | - | - | - | - |
0.6222 | 490 | 0.0097 | - | - | - | - |
0.6349 | 500 | 0.0013 | - | - | - | - |
0.6476 | 510 | 0.0042 | - | - | - | - |
0.6603 | 520 | 0.0034 | - | - | - | - |
0.6730 | 530 | 0.0226 | - | - | - | - |
0.6857 | 540 | 0.0267 | - | - | - | - |
0.6984 | 550 | 0.0007 | - | - | - | - |
0.7111 | 560 | 0.0766 | - | - | - | - |
0.7238 | 570 | 0.2174 | - | - | - | - |
0.7365 | 580 | 0.0089 | - | - | - | - |
0.7492 | 590 | 0.0794 | - | - | - | - |
0.7619 | 600 | 0.0031 | - | - | - | - |
0.7746 | 610 | 0.0499 | - | - | - | - |
0.7873 | 620 | 0.0105 | - | - | - | - |
0.8 | 630 | 0.0097 | - | - | - | - |
0.8127 | 640 | 0.0028 | - | - | - | - |
0.8254 | 650 | 0.0029 | - | - | - | - |
0.8381 | 660 | 0.1811 | - | - | - | - |
0.8508 | 670 | 0.064 | - | - | - | - |
0.8635 | 680 | 0.0139 | - | - | - | - |
0.8762 | 690 | 0.055 | - | - | - | - |
0.8889 | 700 | 0.0013 | - | - | - | - |
0.9016 | 710 | 0.0402 | - | - | - | - |
0.9143 | 720 | 0.0824 | - | - | - | - |
0.9270 | 730 | 0.03 | - | - | - | - |
0.9397 | 740 | 0.0337 | - | - | - | - |
0.9524 | 750 | 0.1192 | - | - | - | - |
0.9651 | 760 | 0.0039 | - | - | - | - |
0.9778 | 770 | 0.004 | - | - | - | - |
0.9905 | 780 | 0.1413 | - | - | - | - |
0.9994 | 787 | - | 0.7851 | 0.7794 | 0.7822 | 0.7863 |
1.0032 | 790 | 0.019 | - | - | - | - |
1.0159 | 800 | 0.0587 | - | - | - | - |
1.0286 | 810 | 0.0186 | - | - | - | - |
1.0413 | 820 | 0.0018 | - | - | - | - |
1.0540 | 830 | 0.0631 | - | - | - | - |
1.0667 | 840 | 0.0127 | - | - | - | - |
1.0794 | 850 | 0.0037 | - | - | - | - |
1.0921 | 860 | 0.0029 | - | - | - | - |
1.1048 | 870 | 0.1437 | - | - | - | - |
1.1175 | 880 | 0.0015 | - | - | - | - |
1.1302 | 890 | 0.0024 | - | - | - | - |
1.1429 | 900 | 0.0133 | - | - | - | - |
1.1556 | 910 | 0.0245 | - | - | - | - |
1.1683 | 920 | 0.0017 | - | - | - | - |
1.1810 | 930 | 0.0007 | - | - | - | - |
1.1937 | 940 | 0.002 | - | - | - | - |
1.2063 | 950 | 0.0044 | - | - | - | - |
1.2190 | 960 | 0.0009 | - | - | - | - |
1.2317 | 970 | 0.01 | - | - | - | - |
1.2444 | 980 | 0.0026 | - | - | - | - |
1.2571 | 990 | 0.0017 | - | - | - | - |
1.2698 | 1000 | 0.0014 | - | - | - | - |
1.2825 | 1010 | 0.0009 | - | - | - | - |
1.2952 | 1020 | 0.0829 | - | - | - | - |
1.3079 | 1030 | 0.0011 | - | - | - | - |
1.3206 | 1040 | 0.012 | - | - | - | - |
1.3333 | 1050 | 0.0019 | - | - | - | - |
1.3460 | 1060 | 0.0007 | - | - | - | - |
1.3587 | 1070 | 0.0141 | - | - | - | - |
1.3714 | 1080 | 0.0003 | - | - | - | - |
1.3841 | 1090 | 0.001 | - | - | - | - |
1.3968 | 1100 | 0.0005 | - | - | - | - |
1.4095 | 1110 | 0.0031 | - | - | - | - |
1.4222 | 1120 | 0.0004 | - | - | - | - |
1.4349 | 1130 | 0.0054 | - | - | - | - |
1.4476 | 1140 | 0.0003 | - | - | - | - |
1.4603 | 1150 | 0.0007 | - | - | - | - |
1.4730 | 1160 | 0.0009 | - | - | - | - |
1.4857 | 1170 | 0.001 | - | - | - | - |
1.4984 | 1180 | 0.0006 | - | - | - | - |
1.5111 | 1190 | 0.0046 | - | - | - | - |
1.5238 | 1200 | 0.0003 | - | - | - | - |
1.5365 | 1210 | 0.0002 | - | - | - | - |
1.5492 | 1220 | 0.004 | - | - | - | - |
1.5619 | 1230 | 0.0017 | - | - | - | - |
1.5746 | 1240 | 0.0003 | - | - | - | - |
1.5873 | 1250 | 0.0027 | - | - | - | - |
1.6 | 1260 | 0.1134 | - | - | - | - |
1.6127 | 1270 | 0.0007 | - | - | - | - |
1.6254 | 1280 | 0.0005 | - | - | - | - |
1.6381 | 1290 | 0.0008 | - | - | - | - |
1.6508 | 1300 | 0.0001 | - | - | - | - |
1.6635 | 1310 | 0.0023 | - | - | - | - |
1.6762 | 1320 | 0.0005 | - | - | - | - |
1.6889 | 1330 | 0.0004 | - | - | - | - |
1.7016 | 1340 | 0.0003 | - | - | - | - |
1.7143 | 1350 | 0.0347 | - | - | - | - |
1.7270 | 1360 | 0.0339 | - | - | - | - |
1.7397 | 1370 | 0.0003 | - | - | - | - |
1.7524 | 1380 | 0.0005 | - | - | - | - |
1.7651 | 1390 | 0.0002 | - | - | - | - |
1.7778 | 1400 | 0.0031 | - | - | - | - |
1.7905 | 1410 | 0.0002 | - | - | - | - |
1.8032 | 1420 | 0.0012 | - | - | - | - |
1.8159 | 1430 | 0.0002 | - | - | - | - |
1.8286 | 1440 | 0.0002 | - | - | - | - |
1.8413 | 1450 | 0.0004 | - | - | - | - |
1.8540 | 1460 | 0.011 | - | - | - | - |
1.8667 | 1470 | 0.0824 | - | - | - | - |
1.8794 | 1480 | 0.0003 | - | - | - | - |
1.8921 | 1490 | 0.0004 | - | - | - | - |
1.9048 | 1500 | 0.0006 | - | - | - | - |
1.9175 | 1510 | 0.015 | - | - | - | - |
1.9302 | 1520 | 0.0004 | - | - | - | - |
1.9429 | 1530 | 0.0004 | - | - | - | - |
1.9556 | 1540 | 0.0011 | - | - | - | - |
1.9683 | 1550 | 0.0003 | - | - | - | - |
1.9810 | 1560 | 0.0006 | - | - | - | - |
1.9937 | 1570 | 0.0042 | - | - | - | - |
2.0 | 1575 | - | 0.7862 | 0.7855 | 0.7852 | 0.7878 |
2.0063 | 1580 | 0.0005 | - | - | - | - |
2.0190 | 1590 | 0.002 | - | - | - | - |
2.0317 | 1600 | 0.0013 | - | - | - | - |
2.0444 | 1610 | 0.0002 | - | - | - | - |
2.0571 | 1620 | 0.0035 | - | - | - | - |
2.0698 | 1630 | 0.0004 | - | - | - | - |
2.0825 | 1640 | 0.0002 | - | - | - | - |
2.0952 | 1650 | 0.0032 | - | - | - | - |
2.1079 | 1660 | 0.0916 | - | - | - | - |
2.1206 | 1670 | 0.0002 | - | - | - | - |
2.1333 | 1680 | 0.0006 | - | - | - | - |
2.1460 | 1690 | 0.0002 | - | - | - | - |
2.1587 | 1700 | 0.0003 | - | - | - | - |
2.1714 | 1710 | 0.0001 | - | - | - | - |
2.1841 | 1720 | 0.0001 | - | - | - | - |
2.1968 | 1730 | 0.0004 | - | - | - | - |
2.2095 | 1740 | 0.0004 | - | - | - | - |
2.2222 | 1750 | 0.0001 | - | - | - | - |
2.2349 | 1760 | 0.0002 | - | - | - | - |
2.2476 | 1770 | 0.0007 | - | - | - | - |
2.2603 | 1780 | 0.0001 | - | - | - | - |
2.2730 | 1790 | 0.0002 | - | - | - | - |
2.2857 | 1800 | 0.0004 | - | - | - | - |
2.2984 | 1810 | 0.0711 | - | - | - | - |
2.3111 | 1820 | 0.0001 | - | - | - | - |
2.3238 | 1830 | 0.0005 | - | - | - | - |
2.3365 | 1840 | 0.0004 | - | - | - | - |
2.3492 | 1850 | 0.0001 | - | - | - | - |
2.3619 | 1860 | 0.0005 | - | - | - | - |
2.3746 | 1870 | 0.0003 | - | - | - | - |
2.3873 | 1880 | 0.0001 | - | - | - | - |
2.4 | 1890 | 0.0002 | - | - | - | - |
2.4127 | 1900 | 0.0001 | - | - | - | - |
2.4254 | 1910 | 0.0002 | - | - | - | - |
2.4381 | 1920 | 0.0002 | - | - | - | - |
2.4508 | 1930 | 0.0002 | - | - | - | - |
2.4635 | 1940 | 0.0004 | - | - | - | - |
2.4762 | 1950 | 0.0001 | - | - | - | - |
2.4889 | 1960 | 0.0002 | - | - | - | - |
2.5016 | 1970 | 0.0002 | - | - | - | - |
2.5143 | 1980 | 0.0001 | - | - | - | - |
2.5270 | 1990 | 0.0001 | - | - | - | - |
2.5397 | 2000 | 0.0002 | - | - | - | - |
2.5524 | 2010 | 0.0023 | - | - | - | - |
2.5651 | 2020 | 0.0002 | - | - | - | - |
2.5778 | 2030 | 0.0001 | - | - | - | - |
2.5905 | 2040 | 0.0003 | - | - | - | - |
2.6032 | 2050 | 0.0003 | - | - | - | - |
2.6159 | 2060 | 0.0002 | - | - | - | - |
2.6286 | 2070 | 0.0001 | - | - | - | - |
2.6413 | 2080 | 0.0 | - | - | - | - |
2.6540 | 2090 | 0.0001 | - | - | - | - |
2.6667 | 2100 | 0.0001 | - | - | - | - |
2.6794 | 2110 | 0.0001 | - | - | - | - |
2.6921 | 2120 | 0.0001 | - | - | - | - |
2.7048 | 2130 | 0.0001 | - | - | - | - |
2.7175 | 2140 | 0.0048 | - | - | - | - |
2.7302 | 2150 | 0.0005 | - | - | - | - |
2.7429 | 2160 | 0.0001 | - | - | - | - |
2.7556 | 2170 | 0.0001 | - | - | - | - |
2.7683 | 2180 | 0.0001 | - | - | - | - |
2.7810 | 2190 | 0.0001 | - | - | - | - |
2.7937 | 2200 | 0.0001 | - | - | - | - |
2.8063 | 2210 | 0.0001 | - | - | - | - |
2.8190 | 2220 | 0.0001 | - | - | - | - |
2.8317 | 2230 | 0.0002 | - | - | - | - |
2.8444 | 2240 | 0.0036 | - | - | - | - |
2.8571 | 2250 | 0.0001 | - | - | - | - |
2.8698 | 2260 | 0.0368 | - | - | - | - |
2.8825 | 2270 | 0.0003 | - | - | - | - |
2.8952 | 2280 | 0.0002 | - | - | - | - |
2.9079 | 2290 | 0.0001 | - | - | - | - |
2.9206 | 2300 | 0.0005 | - | - | - | - |
2.9333 | 2310 | 0.0001 | - | - | - | - |
2.9460 | 2320 | 0.0001 | - | - | - | - |
2.9587 | 2330 | 0.0003 | - | - | - | - |
2.9714 | 2340 | 0.0001 | - | - | - | - |
2.9841 | 2350 | 0.0001 | - | - | - | - |
2.9968 | 2360 | 0.0002 | - | - | - | - |
2.9994 | 2362 | - | 0.7864 | 0.7805 | 0.7838 | 0.7852 |
3.0095 | 2370 | 0.0025 | - | - | - | - |
3.0222 | 2380 | 0.0002 | - | - | - | - |
3.0349 | 2390 | 0.0001 | - | - | - | - |
3.0476 | 2400 | 0.0001 | - | - | - | - |
3.0603 | 2410 | 0.0001 | - | - | - | - |
3.0730 | 2420 | 0.0001 | - | - | - | - |
3.0857 | 2430 | 0.0001 | - | - | - | - |
3.0984 | 2440 | 0.0002 | - | - | - | - |
3.1111 | 2450 | 0.0116 | - | - | - | - |
3.1238 | 2460 | 0.0002 | - | - | - | - |
3.1365 | 2470 | 0.0001 | - | - | - | - |
3.1492 | 2480 | 0.0001 | - | - | - | - |
3.1619 | 2490 | 0.0001 | - | - | - | - |
3.1746 | 2500 | 0.0001 | - | - | - | - |
3.1873 | 2510 | 0.0001 | - | - | - | - |
3.2 | 2520 | 0.0001 | - | - | - | - |
3.2127 | 2530 | 0.0001 | - | - | - | - |
3.2254 | 2540 | 0.0001 | - | - | - | - |
3.2381 | 2550 | 0.0002 | - | - | - | - |
3.2508 | 2560 | 0.0001 | - | - | - | - |
3.2635 | 2570 | 0.0001 | - | - | - | - |
3.2762 | 2580 | 0.0001 | - | - | - | - |
3.2889 | 2590 | 0.0001 | - | - | - | - |
3.3016 | 2600 | 0.063 | - | - | - | - |
3.3143 | 2610 | 0.0001 | - | - | - | - |
3.3270 | 2620 | 0.0001 | - | - | - | - |
3.3397 | 2630 | 0.0001 | - | - | - | - |
3.3524 | 2640 | 0.0001 | - | - | - | - |
3.3651 | 2650 | 0.0002 | - | - | - | - |
3.3778 | 2660 | 0.0001 | - | - | - | - |
3.3905 | 2670 | 0.0001 | - | - | - | - |
3.4032 | 2680 | 0.0001 | - | - | - | - |
3.4159 | 2690 | 0.0001 | - | - | - | - |
3.4286 | 2700 | 0.0001 | - | - | - | - |
3.4413 | 2710 | 0.0001 | - | - | - | - |
3.4540 | 2720 | 0.0002 | - | - | - | - |
3.4667 | 2730 | 0.0001 | - | - | - | - |
3.4794 | 2740 | 0.0001 | - | - | - | - |
3.4921 | 2750 | 0.0001 | - | - | - | - |
3.5048 | 2760 | 0.0001 | - | - | - | - |
3.5175 | 2770 | 0.0002 | - | - | - | - |
3.5302 | 2780 | 0.0001 | - | - | - | - |
3.5429 | 2790 | 0.0001 | - | - | - | - |
3.5556 | 2800 | 0.0001 | - | - | - | - |
3.5683 | 2810 | 0.0001 | - | - | - | - |
3.5810 | 2820 | 0.0001 | - | - | - | - |
3.5937 | 2830 | 0.0001 | - | - | - | - |
3.6063 | 2840 | 0.0001 | - | - | - | - |
3.6190 | 2850 | 0.0 | - | - | - | - |
3.6317 | 2860 | 0.0001 | - | - | - | - |
3.6444 | 2870 | 0.0001 | - | - | - | - |
3.6571 | 2880 | 0.0001 | - | - | - | - |
3.6698 | 2890 | 0.0001 | - | - | - | - |
3.6825 | 2900 | 0.0001 | - | - | - | - |
3.6952 | 2910 | 0.0001 | - | - | - | - |
3.7079 | 2920 | 0.0001 | - | - | - | - |
3.7206 | 2930 | 0.0003 | - | - | - | - |
3.7333 | 2940 | 0.0001 | - | - | - | - |
3.7460 | 2950 | 0.0001 | - | - | - | - |
3.7587 | 2960 | 0.0001 | - | - | - | - |
3.7714 | 2970 | 0.0002 | - | - | - | - |
3.7841 | 2980 | 0.0001 | - | - | - | - |
3.7968 | 2990 | 0.0001 | - | - | - | - |
3.8095 | 3000 | 0.0001 | - | - | - | - |
3.8222 | 3010 | 0.0001 | - | - | - | - |
3.8349 | 3020 | 0.0002 | - | - | - | - |
3.8476 | 3030 | 0.0001 | - | - | - | - |
3.8603 | 3040 | 0.0001 | - | - | - | - |
3.8730 | 3050 | 0.0214 | - | - | - | - |
3.8857 | 3060 | 0.0001 | - | - | - | - |
3.8984 | 3070 | 0.0001 | - | - | - | - |
3.9111 | 3080 | 0.0001 | - | - | - | - |
3.9238 | 3090 | 0.0001 | - | - | - | - |
3.9365 | 3100 | 0.0001 | - | - | - | - |
3.9492 | 3110 | 0.0001 | - | - | - | - |
3.9619 | 3120 | 0.0001 | - | - | - | - |
3.9746 | 3130 | 0.0001 | - | - | - | - |
3.9873 | 3140 | 0.0001 | - | - | - | - |
3.9975 | 3148 | - | 0.7867 | 0.7838 | 0.7827 | 0.7843 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.12.2
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.2.0+cu121
- Accelerate: 0.31.0
- Datasets: 2.19.1
- Tokenizers: 0.19.1
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-m3Evaluation results
- Cosine Accuracy@1 on dim 1024self-reported0.717
- Cosine Accuracy@3 on dim 1024self-reported0.831
- Cosine Accuracy@5 on dim 1024self-reported0.870
- Cosine Accuracy@10 on dim 1024self-reported0.914
- Cosine Precision@1 on dim 1024self-reported0.717
- Cosine Precision@3 on dim 1024self-reported0.277
- Cosine Precision@5 on dim 1024self-reported0.174
- Cosine Precision@10 on dim 1024self-reported0.091
- Cosine Recall@1 on dim 1024self-reported0.717
- Cosine Recall@3 on dim 1024self-reported0.831