diff --git "a/README.md" "b/README.md" --- "a/README.md" +++ "b/README.md" @@ -1,3 +1,2322 @@ ---- -license: apache-2.0 ---- +--- +tags: +- ColBERT +- PyLate +- sentence-transformers +- sentence-similarity +- feature-extraction +- generated_from_trainer +- dataset_size:598752 +- loss:KDLossWrapper +base_model: cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR +pipeline_tag: sentence-similarity +library_name: PyLate +--- + +# PyLate model based on cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR + +This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR](https://huggingface.co/cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR). It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator. + +## Model Details + +### Model Description +- **Model Type:** PyLate model +- **Base model:** [cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR](https://huggingface.co/cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR) +- **Document Length:** 96 tokens +- **Query Length:** 96 tokens +- **Output Dimensionality:** 128 tokens +- **Similarity Function:** MaxSim + + + + +### Model Sources + +- **Documentation:** [PyLate Documentation](https://lightonai.github.io/pylate/) +- **Repository:** [PyLate on GitHub](https://github.com/lightonai/pylate) +- **Hugging Face:** [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate) + +### Full Model Architecture + +``` +ColBERT( + (0): Transformer({'max_seq_length': 95, 'do_lower_case': False, 'architecture': 'PeftModelForFeatureExtraction'}) + (1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False}) +) +``` + +## Usage +First install the PyLate library: + +```bash +pip install -U pylate +``` + +### Retrieval + +Use this model with PyLate to index and retrieve documents. The index uses [FastPLAID](https://github.com/lightonai/fast-plaid) for efficient similarity search. + +#### Indexing documents + +Load the ColBERT model and initialize the PLAID index, then encode and index your documents: + +```python +from pylate import indexes, models, retrieve + +# Step 1: Load the ColBERT model +model = models.ColBERT( + model_name_or_path="pylate_model_id", +) + +# Step 2: Initialize the PLAID index +index = indexes.PLAID( + index_folder="pylate-index", + index_name="index", + override=True, # This overwrites the existing index if any +) + +# Step 3: Encode the documents +documents_ids = ["1", "2", "3"] +documents = ["document 1 text", "document 2 text", "document 3 text"] + +documents_embeddings = model.encode( + documents, + batch_size=32, + is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries + show_progress_bar=True, +) + +# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids +index.add_documents( + documents_ids=documents_ids, + documents_embeddings=documents_embeddings, +) +``` + +Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it: + +```python +# To load an index, simply instantiate it with the correct folder/name and without overriding it +index = indexes.PLAID( + index_folder="pylate-index", + index_name="index", +) +``` + +#### Retrieving top-k documents for queries + +Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries. +To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores: + +```python +# Step 1: Initialize the ColBERT retriever +retriever = retrieve.ColBERT(index=index) + +# Step 2: Encode the queries +queries_embeddings = model.encode( + ["query for document 3", "query for document 1"], + batch_size=32, + is_query=True, # # Ensure that it is set to False to indicate that these are queries + show_progress_bar=True, +) + +# Step 3: Retrieve top-k documents +scores = retriever.retrieve( + queries_embeddings=queries_embeddings, + k=10, # Retrieve the top 10 matches for each query +) +``` + +### Reranking +If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank: + +```python +from pylate import rank, models + +queries = [ + "query A", + "query B", +] + +documents = [ + ["document A", "document B"], + ["document 1", "document C", "document B"], +] + +documents_ids = [ + [1, 2], + [1, 3, 2], +] + +model = models.ColBERT( + model_name_or_path="pylate_model_id", +) + +queries_embeddings = model.encode( + queries, + is_query=True, +) + +documents_embeddings = model.encode( + documents, + is_query=False, +) + +reranked_documents = rank.rerank( + documents_ids=documents_ids, + queries_embeddings=queries_embeddings, + documents_embeddings=documents_embeddings, +) +``` + + + + + + + + + + + +## Training Details + +### Training Dataset + +#### Unnamed Dataset + + +* Size: 598,752 training samples +* Columns: query, positive, query_id, target_id, group_id, negative_0, negative_1, and negative_2 +* Approximate statistics based on the first 1000 samples: + | | query | positive | query_id | target_id | group_id | negative_0 | negative_1 | negative_2 | + 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+ | type | string | string | string | int | int | string | string | string | + | details | | | | | | | | | +* Samples: + | query | positive | query_id | target_id | group_id | negative_0 | negative_1 | negative_2 | + |:------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:-------------------------|:----------------------|:----------------------|:---------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------| + | åøƒę“›čŠ¬ ē‰‡å‰‚ [åÆꙮ芬] | ibuprofen oral tablet [kepufen] | q36653233_0 | 35870422 | 1177480 | ibuprofen topical ointment [vesicum] | ibuprofen extended release oral tablet [ibu 1a pharma] | ibuprofen oral tablet [jamp-ibuprofen] | + | avapro hct 300/12.5 tablet, 30 | hydrochlorothiazide 12.5 mg / irbesartan 300 mg oral tablet [avapro hct] box of 30 | q43362350_0 | 2936189 | 19081559 | hydrochlorothiazide 25 mg / irbesartan 300 mg delayed release oral tablet [irbesartan hydrochlorothiazide teva] box of 28 | hydrochlorothiazide 25 mg / irbesartan 300 mg oral tablet [irbesartan bz] box of 98 | hydrochlorothiazide 25 mg / irbesartan 300 mg delayed release oral tablet [karvezide] box of 280 | + | povidone-iodine (pfizer (perth)) 10% application, 500 ml, bottle | 500 ml povidone-iodine 100 mg/ml transdermal system by pfizer | q43321701_0 | 2907294 | 19083433 | 30 ml povidone-iodine 100 mg/ml topical solution by pfizer | 125 ml povidone-iodine 100 mg/ml topical solution [povidone iodee mylan] box of 1 by mylan | 100 ml povidone-iodine 100 mg/ml topical solution by bittermedizin | +* Loss: thirawat_trainer.training.colbert.KDLossWrapper + +### Evaluation Dataset + +#### Unnamed Dataset + + +* Size: 2,270 evaluation samples +* Columns: query, positive, query_id, target_id, group_id, negative_0, negative_1, and negative_2 +* Approximate statistics based on the first 1000 samples: + | | query | positive | query_id | target_id | group_id | negative_0 | negative_1 | negative_2 | + 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+ | type | string | string | string | int | int | string | string | string | + | details | | | | | | | | | +* Samples: + | query | positive | query_id | target_id | group_id | negative_0 | negative_1 | negative_2 | + |:-----------------------------------------------------|:---------------------------------------------------|:-------------------------|:----------------------|:----------------------|:------------------------------------------------------|:-----------------------------------------------------------|:--------------------------------------------------------------------| + | fentanyl 100 microgram/hour patch, 5 | fentanyl 0.1 mg/hr transdermal system | q43342123_0 | 42902560 | 1154029 | fentanyl 0.158 mg/actuat nasal spray | fentanyl 0.05 mg/ml nasal spray | fentanyl 1.89 mg unknown combination pack medical aids | + | sodium benzoate 1.83g/5ml oral solution | benzoate 366 mg/ml oral solution | q21235913_0 | 21119205 | 21119204 | sodium chloride 23.2 mg/ml oral solution | fluticasone 0.125 mg/actuat inhalation powder | chlorhexidine 0.00001 mg/ml oral solution | + | meloxicam - meloxicam tablet | meloxicam 15 mg oral tablet | q45623828_0 | 1150347 | 1150347 | econazole 0.0314 mg/mg topical cream | nabumetone 400 mg oral tablet | domestic cow skin extract 50 mg/ml injectable solution | +* Loss: thirawat_trainer.training.colbert.KDLossWrapper + +### Training Hyperparameters +#### Non-Default Hyperparameters + +- `eval_strategy`: epoch +- `per_device_train_batch_size`: 64 +- `gradient_accumulation_steps`: 2 +- `learning_rate`: 2e-05 +- `num_train_epochs`: 2 +- `warmup_ratio`: 0.1 +- `bf16`: True +- `dataloader_num_workers`: 4 +- `remove_unused_columns`: False + +#### All Hyperparameters +
Click to expand + +- `overwrite_output_dir`: False +- `do_predict`: False +- `eval_strategy`: epoch +- `prediction_loss_only`: True +- `per_device_train_batch_size`: 64 +- `per_device_eval_batch_size`: 8 +- `per_gpu_train_batch_size`: None +- `per_gpu_eval_batch_size`: None +- `gradient_accumulation_steps`: 2 +- `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`: 2 +- `max_steps`: -1 +- `lr_scheduler_type`: linear +- `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`: None +- `local_rank`: 0 +- `ddp_backend`: None +- `tpu_num_cores`: None +- `tpu_metrics_debug`: False +- `debug`: [] +- `dataloader_drop_last`: False +- `dataloader_num_workers`: 4 +- `dataloader_prefetch_factor`: None +- `past_index`: -1 +- `disable_tqdm`: False +- `remove_unused_columns`: False +- `label_names`: None +- `load_best_model_at_end`: False +- `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} +- `parallelism_config`: 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 +- `hub_revision`: None +- `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 +- `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 +- `liger_kernel_config`: None +- `eval_use_gather_object`: False +- `average_tokens_across_devices`: False +- `prompts`: None +- `batch_sampler`: batch_sampler +- `multi_dataset_batch_sampler`: proportional +- `router_mapping`: {} +- `learning_rate_mapping`: {} + +
+ +### Training Logs +
Click to expand + +| Epoch | Step | Training Loss | Validation Loss | +|:------:|:----:|:-------------:|:---------------:| +| 0.0011 | 5 | 50.3236 | - | +| 0.0021 | 10 | 59.3515 | - | +| 0.0032 | 15 | 62.6096 | - | +| 0.0043 | 20 | 71.224 | - | +| 0.0053 | 25 | 58.249 | - | +| 0.0064 | 30 | 53.1647 | - | +| 0.0075 | 35 | 74.0864 | - | +| 0.0086 | 40 | 68.1396 | - | +| 0.0096 | 45 | 67.5764 | - | +| 0.0107 | 50 | 59.9784 | - | +| 0.0118 | 55 | 51.3131 | - | +| 0.0128 | 60 | 68.0457 | - | +| 0.0139 | 65 | 58.9018 | - | +| 0.0150 | 70 | 59.4911 | - | +| 0.0160 | 75 | 58.0199 | - | +| 0.0171 | 80 | 63.7679 | - | +| 0.0182 | 85 | 60.7314 | - | +| 0.0192 | 90 | 77.8096 | - | +| 0.0203 | 95 | 66.2088 | - | +| 0.0214 | 100 | 65.6054 | - | +| 0.0224 | 105 | 80.6449 | - | +| 0.0235 | 110 | 69.9855 | - | +| 0.0246 | 115 | 56.6307 | - | +| 0.0257 | 120 | 46.5356 | - | +| 0.0267 | 125 | 57.7131 | - | +| 0.0278 | 130 | 49.0737 | - | +| 0.0289 | 135 | 50.7495 | - | +| 0.0299 | 140 | 57.1029 | - | +| 0.0310 | 145 | 45.4298 | - | +| 0.0321 | 150 | 57.8409 | - | +| 0.0331 | 155 | 68.8388 | - | +| 0.0342 | 160 | 64.5809 | - | +| 0.0353 | 165 | 54.5407 | - | +| 0.0363 | 170 | 48.341 | - | +| 0.0374 | 175 | 44.3806 | - | +| 0.0385 | 180 | 59.123 | - | +| 0.0395 | 185 | 59.4863 | - | +| 0.0406 | 190 | 65.4498 | - | +| 0.0417 | 195 | 52.9526 | - | +| 0.0428 | 200 | 46.6744 | - | +| 0.0438 | 205 | 37.1728 | - | +| 0.0449 | 210 | 63.3973 | - | +| 0.0460 | 215 | 51.1136 | - | +| 0.0470 | 220 | 40.9712 | - | +| 0.0481 | 225 | 46.4765 | - | +| 0.0492 | 230 | 47.3894 | - | +| 0.0502 | 235 | 36.866 | - | +| 0.0513 | 240 | 32.3704 | - | +| 0.0524 | 245 | 36.0298 | - | +| 0.0534 | 250 | 43.7503 | - | +| 0.0545 | 255 | 33.9929 | - | +| 0.0556 | 260 | 38.4924 | - | +| 0.0566 | 265 | 30.8329 | - | +| 0.0577 | 270 | 29.0382 | - | +| 0.0588 | 275 | 25.2408 | - | +| 0.0599 | 280 | 30.9856 | - | +| 0.0609 | 285 | 25.987 | - | +| 0.0620 | 290 | 24.4493 | - | +| 0.0631 | 295 | 25.6893 | - | +| 0.0641 | 300 | 29.4356 | - | +| 0.0652 | 305 | 22.2 | - | +| 0.0663 | 310 | 24.3048 | - | +| 0.0673 | 315 | 18.5714 | - | +| 0.0684 | 320 | 17.4822 | - | +| 0.0695 | 325 | 15.8966 | - | +| 0.0705 | 330 | 14.8771 | - | +| 0.0716 | 335 | 16.0905 | - | +| 0.0727 | 340 | 11.9737 | - | +| 0.0737 | 345 | 12.1407 | - | +| 0.0748 | 350 | 11.5492 | - | +| 0.0759 | 355 | 13.0664 | - | +| 0.0770 | 360 | 10.0428 | - | +| 0.0780 | 365 | 9.2962 | - | +| 0.0791 | 370 | 8.6304 | - | +| 0.0802 | 375 | 5.7954 | - | +| 0.0812 | 380 | 6.0213 | - | +| 0.0823 | 385 | 6.8873 | - | +| 0.0834 | 390 | 5.6347 | - | +| 0.0844 | 395 | 5.7346 | - | +| 0.0855 | 400 | 4.2817 | - | +| 0.0866 | 405 | 3.9817 | - | +| 0.0876 | 410 | 3.1871 | - | +| 0.0887 | 415 | 3.2088 | - | +| 0.0898 | 420 | 3.5216 | - | +| 0.0909 | 425 | 2.3657 | - | +| 0.0919 | 430 | 2.3054 | - | +| 0.0930 | 435 | 2.174 | - | +| 0.0941 | 440 | 2.4017 | - | +| 0.0951 | 445 | 2.1189 | - | +| 0.0962 | 450 | 2.3965 | - | +| 0.0973 | 455 | 1.7638 | - | +| 0.0983 | 460 | 1.4081 | - | +| 0.0994 | 465 | 1.5501 | - | +| 0.1005 | 470 | 1.4015 | - | +| 0.1015 | 475 | 1.3726 | - | +| 0.1026 | 480 | 1.1044 | - | +| 0.1037 | 485 | 1.1651 | - | +| 0.1047 | 490 | 1.1674 | - | +| 0.1058 | 495 | 1.2274 | - | +| 0.1069 | 500 | 1.0606 | - | +| 0.1080 | 505 | 1.2207 | - | +| 0.1090 | 510 | 1.12 | - | +| 0.1101 | 515 | 0.9925 | - | +| 0.1112 | 520 | 0.9542 | - | +| 0.1122 | 525 | 1.1911 | - | +| 0.1133 | 530 | 1.2418 | - | +| 0.1144 | 535 | 1.0926 | - | +| 0.1154 | 540 | 0.9052 | - | +| 0.1165 | 545 | 0.9725 | - | +| 0.1176 | 550 | 1.0631 | - | +| 0.1186 | 555 | 1.0069 | - | +| 0.1197 | 560 | 0.9301 | - | +| 0.1208 | 565 | 0.7413 | - | +| 0.1218 | 570 | 1.0359 | - | +| 0.1229 | 575 | 0.9793 | - | +| 0.1240 | 580 | 0.7139 | - | +| 0.1251 | 585 | 1.0595 | - | +| 0.1261 | 590 | 0.8869 | - | +| 0.1272 | 595 | 0.8318 | - | +| 0.1283 | 600 | 0.7328 | - | +| 0.1293 | 605 | 0.7785 | - | +| 0.1304 | 610 | 0.8507 | - | +| 0.1315 | 615 | 1.0039 | - | +| 0.1325 | 620 | 0.7514 | - | +| 0.1336 | 625 | 0.6666 | - | +| 0.1347 | 630 | 0.6639 | - | +| 0.1357 | 635 | 0.77 | - | +| 0.1368 | 640 | 0.8079 | - | +| 0.1379 | 645 | 0.8061 | - | +| 0.1389 | 650 | 0.7666 | - | +| 0.1400 | 655 | 0.7835 | - | +| 0.1411 | 660 | 0.7319 | - | +| 0.1422 | 665 | 0.5945 | - | +| 0.1432 | 670 | 0.7008 | - | +| 0.1443 | 675 | 0.9728 | - | +| 0.1454 | 680 | 0.7346 | - | +| 0.1464 | 685 | 0.8463 | - | +| 0.1475 | 690 | 0.7251 | - | +| 0.1486 | 695 | 0.7444 | - | +| 0.1496 | 700 | 0.6496 | - | +| 0.1507 | 705 | 0.7623 | - | +| 0.1518 | 710 | 0.8285 | - | +| 0.1528 | 715 | 0.886 | - | +| 0.1539 | 720 | 0.7274 | - | +| 0.1550 | 725 | 0.8669 | - | +| 0.1560 | 730 | 0.6741 | - | +| 0.1571 | 735 | 0.7396 | - | +| 0.1582 | 740 | 0.7197 | - | +| 0.1593 | 745 | 0.9099 | - | +| 0.1603 | 750 | 0.7344 | - | +| 0.1614 | 755 | 0.6107 | - | +| 0.1625 | 760 | 0.6929 | - | +| 0.1635 | 765 | 0.7019 | - | +| 0.1646 | 770 | 0.5759 | - | +| 0.1657 | 775 | 0.5736 | - | +| 0.1667 | 780 | 0.5878 | - | +| 0.1678 | 785 | 0.7349 | - | +| 0.1689 | 790 | 0.7691 | - | +| 0.1699 | 795 | 0.6228 | - | +| 0.1710 | 800 | 0.6394 | - | +| 0.1721 | 805 | 0.624 | - | +| 0.1732 | 810 | 0.6487 | - | +| 0.1742 | 815 | 0.6001 | - | +| 0.1753 | 820 | 0.6685 | - | +| 0.1764 | 825 | 0.8176 | - | +| 0.1774 | 830 | 0.6417 | - | +| 0.1785 | 835 | 0.6353 | - | +| 0.1796 | 840 | 0.5946 | - | +| 0.1806 | 845 | 0.6122 | - | +| 0.1817 | 850 | 0.6499 | - | +| 0.1828 | 855 | 0.601 | - | +| 0.1838 | 860 | 0.5404 | - | +| 0.1849 | 865 | 0.6574 | - | +| 0.1860 | 870 | 0.5377 | - | +| 0.1870 | 875 | 0.5537 | - | +| 0.1881 | 880 | 0.7377 | - | +| 0.1892 | 885 | 0.8466 | - | +| 0.1903 | 890 | 0.4808 | - | +| 0.1913 | 895 | 0.5482 | - | +| 0.1924 | 900 | 0.6588 | - | +| 0.1935 | 905 | 0.6953 | - | +| 0.1945 | 910 | 0.4803 | - | +| 0.1956 | 915 | 0.6869 | - | +| 0.1967 | 920 | 0.5393 | - | +| 0.1977 | 925 | 0.4829 | - | +| 0.1988 | 930 | 0.5071 | - | +| 0.1999 | 935 | 0.6828 | - | +| 0.2009 | 940 | 0.6468 | - | +| 0.2020 | 945 | 0.6169 | - | +| 0.2031 | 950 | 0.5389 | - | +| 0.2041 | 955 | 0.4527 | - | +| 0.2052 | 960 | 0.5405 | - | +| 0.2063 | 965 | 0.6583 | - | +| 0.2074 | 970 | 0.7037 | - | +| 0.2084 | 975 | 0.564 | - | +| 0.2095 | 980 | 0.4531 | - | +| 0.2106 | 985 | 0.5454 | - | +| 0.2116 | 990 | 0.4836 | - | +| 0.2127 | 995 | 0.5458 | - | +| 0.2138 | 1000 | 0.5202 | - | +| 0.2148 | 1005 | 0.5532 | - | +| 0.2159 | 1010 | 0.63 | - | +| 0.2170 | 1015 | 0.6299 | - | +| 0.2180 | 1020 | 0.5405 | - | +| 0.2191 | 1025 | 0.4379 | - | +| 0.2202 | 1030 | 0.7446 | - | +| 0.2212 | 1035 | 0.6652 | - | +| 0.2223 | 1040 | 0.6873 | - | +| 0.2234 | 1045 | 0.4904 | - | +| 0.2245 | 1050 | 0.5763 | - | +| 0.2255 | 1055 | 0.5801 | - | +| 0.2266 | 1060 | 0.518 | - | +| 0.2277 | 1065 | 0.5928 | - | +| 0.2287 | 1070 | 0.5575 | - | +| 0.2298 | 1075 | 0.497 | - | +| 0.2309 | 1080 | 0.4607 | - | +| 0.2319 | 1085 | 0.5531 | - | +| 0.2330 | 1090 | 0.5372 | - | +| 0.2341 | 1095 | 0.4641 | - | +| 0.2351 | 1100 | 0.4724 | - | +| 0.2362 | 1105 | 0.4422 | - | +| 0.2373 | 1110 | 0.4571 | - | +| 0.2383 | 1115 | 0.5104 | - | +| 0.2394 | 1120 | 0.5217 | - | +| 0.2405 | 1125 | 0.4481 | - | +| 0.2416 | 1130 | 0.5244 | - | +| 0.2426 | 1135 | 0.4593 | - | +| 0.2437 | 1140 | 0.5566 | - | +| 0.2448 | 1145 | 0.5473 | - | +| 0.2458 | 1150 | 0.3649 | - | +| 0.2469 | 1155 | 0.5466 | - | +| 0.2480 | 1160 | 0.4687 | - | +| 0.2490 | 1165 | 0.521 | - | +| 0.2501 | 1170 | 0.4539 | - | +| 0.2512 | 1175 | 0.5975 | - | +| 0.2522 | 1180 | 0.4108 | - | +| 0.2533 | 1185 | 0.4052 | - | +| 0.2544 | 1190 | 0.3805 | - | +| 0.2555 | 1195 | 0.508 | - | +| 0.2565 | 1200 | 0.4517 | - | +| 0.2576 | 1205 | 0.5546 | - | +| 0.2587 | 1210 | 0.5427 | - | +| 0.2597 | 1215 | 0.4869 | - | +| 0.2608 | 1220 | 0.5849 | - | +| 0.2619 | 1225 | 0.4608 | - | +| 0.2629 | 1230 | 0.6211 | - | +| 0.2640 | 1235 | 0.5136 | - | +| 0.2651 | 1240 | 0.4788 | - | +| 0.2661 | 1245 | 0.4763 | - | +| 0.2672 | 1250 | 0.5398 | - | +| 0.2683 | 1255 | 0.5782 | - | +| 0.2693 | 1260 | 0.4568 | - | +| 0.2704 | 1265 | 0.3874 | - | +| 0.2715 | 1270 | 0.4604 | - | +| 0.2726 | 1275 | 0.4202 | - | +| 0.2736 | 1280 | 0.4467 | - | +| 0.2747 | 1285 | 0.3631 | - | +| 0.2758 | 1290 | 0.5917 | - | +| 0.2768 | 1295 | 0.4315 | - | +| 0.2779 | 1300 | 0.3898 | - | +| 0.2790 | 1305 | 0.5546 | - | +| 0.2800 | 1310 | 0.4766 | - | +| 0.2811 | 1315 | 0.5883 | - | +| 0.2822 | 1320 | 0.4837 | - | +| 0.2832 | 1325 | 0.4576 | - | +| 0.2843 | 1330 | 0.3894 | - | +| 0.2854 | 1335 | 0.5239 | - | +| 0.2864 | 1340 | 0.4255 | - | +| 0.2875 | 1345 | 0.3944 | - | +| 0.2886 | 1350 | 0.6219 | - | +| 0.2897 | 1355 | 0.4791 | - | +| 0.2907 | 1360 | 0.4101 | - | +| 0.2918 | 1365 | 0.4422 | - | +| 0.2929 | 1370 | 0.4426 | - | +| 0.2939 | 1375 | 0.4108 | - | +| 0.2950 | 1380 | 0.4734 | - | +| 0.2961 | 1385 | 0.4678 | - | +| 0.2971 | 1390 | 0.4334 | - | +| 0.2982 | 1395 | 0.4211 | - | +| 0.2993 | 1400 | 0.5807 | - | +| 0.3003 | 1405 | 0.5956 | - | +| 0.3014 | 1410 | 0.4175 | - | +| 0.3025 | 1415 | 0.4233 | - | +| 0.3035 | 1420 | 0.4118 | - | +| 0.3046 | 1425 | 0.3919 | - | +| 0.3057 | 1430 | 0.5152 | - | +| 0.3068 | 1435 | 0.393 | - | +| 0.3078 | 1440 | 0.3765 | - | +| 0.3089 | 1445 | 0.4476 | - | +| 0.3100 | 1450 | 0.5094 | - | +| 0.3110 | 1455 | 0.4496 | - | +| 0.3121 | 1460 | 0.4305 | - | +| 0.3132 | 1465 | 0.495 | - | +| 0.3142 | 1470 | 0.5561 | - | +| 0.3153 | 1475 | 0.4432 | - | +| 0.3164 | 1480 | 0.3382 | - | +| 0.3174 | 1485 | 0.4209 | - | +| 0.3185 | 1490 | 0.3776 | - | +| 0.3196 | 1495 | 0.4271 | - | +| 0.3206 | 1500 | 0.5628 | - | +| 0.3217 | 1505 | 0.4438 | - | +| 0.3228 | 1510 | 0.3863 | - | +| 0.3239 | 1515 | 0.5629 | - | +| 0.3249 | 1520 | 0.4851 | - | +| 0.3260 | 1525 | 0.3386 | - | +| 0.3271 | 1530 | 0.4546 | - | +| 0.3281 | 1535 | 0.4147 | - | +| 0.3292 | 1540 | 0.403 | - | +| 0.3303 | 1545 | 0.4155 | - | +| 0.3313 | 1550 | 0.4831 | - | +| 0.3324 | 1555 | 0.388 | - | +| 0.3335 | 1560 | 0.4184 | - | +| 0.3345 | 1565 | 0.3645 | - | +| 0.3356 | 1570 | 0.4653 | - | +| 0.3367 | 1575 | 0.5196 | - | +| 0.3378 | 1580 | 0.5252 | - | +| 0.3388 | 1585 | 0.6971 | - | +| 0.3399 | 1590 | 0.3881 | - | +| 0.3410 | 1595 | 0.4302 | - | +| 0.3420 | 1600 | 0.3701 | - 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| +| 0.3773 | 1765 | 0.3876 | - | +| 0.3784 | 1770 | 0.3393 | - | +| 0.3794 | 1775 | 0.4276 | - | +| 0.3805 | 1780 | 0.4641 | - | +| 0.3816 | 1785 | 0.4248 | - | +| 0.3826 | 1790 | 0.3601 | - | +| 0.3837 | 1795 | 0.3384 | - | +| 0.3848 | 1800 | 0.3598 | - | +| 0.3858 | 1805 | 0.4363 | - | +| 0.3869 | 1810 | 0.335 | - | +| 0.3880 | 1815 | 0.3892 | - | +| 0.3891 | 1820 | 0.4103 | - | +| 0.3901 | 1825 | 0.3959 | - | +| 0.3912 | 1830 | 0.3593 | - | +| 0.3923 | 1835 | 0.4366 | - | +| 0.3933 | 1840 | 0.3633 | - | +| 0.3944 | 1845 | 0.4333 | - | +| 0.3955 | 1850 | 0.4825 | - | +| 0.3965 | 1855 | 0.3041 | - | +| 0.3976 | 1860 | 0.3416 | - | +| 0.3987 | 1865 | 0.4578 | - | +| 0.3997 | 1870 | 0.5441 | - | +| 0.4008 | 1875 | 0.5124 | - | +| 0.4019 | 1880 | 0.4026 | - | +| 0.4029 | 1885 | 0.4537 | - | +| 0.4040 | 1890 | 0.3983 | - | +| 0.4051 | 1895 | 0.3894 | - | +| 0.4062 | 1900 | 0.4747 | - | +| 0.4072 | 1905 | 0.4042 | - | +| 0.4083 | 1910 | 0.3477 | - | +| 0.4094 | 1915 | 0.4512 | - | +| 0.4104 | 1920 | 0.3451 | - | +| 0.4115 | 1925 | 0.3983 | - | +| 0.4126 | 1930 | 0.62 | - | +| 0.4136 | 1935 | 0.4632 | - | +| 0.4147 | 1940 | 0.3757 | - | +| 0.4158 | 1945 | 0.4752 | - | +| 0.4168 | 1950 | 0.453 | - | +| 0.4179 | 1955 | 0.4348 | - | +| 0.4190 | 1960 | 0.3944 | - | +| 0.4201 | 1965 | 0.3702 | - | +| 0.4211 | 1970 | 0.3888 | - | +| 0.4222 | 1975 | 0.3968 | - | +| 0.4233 | 1980 | 0.3609 | - | +| 0.4243 | 1985 | 0.4328 | - | +| 0.4254 | 1990 | 0.516 | - | +| 0.4265 | 1995 | 0.3605 | - | +| 0.4275 | 2000 | 0.3094 | - | +| 0.4286 | 2005 | 0.3866 | - | +| 0.4297 | 2010 | 0.3943 | - | +| 0.4307 | 2015 | 0.3931 | - | +| 0.4318 | 2020 | 0.3728 | - | +| 0.4329 | 2025 | 0.2953 | - | +| 0.4339 | 2030 | 0.3547 | - | +| 0.4350 | 2035 | 0.4275 | - | +| 0.4361 | 2040 | 0.2959 | - | +| 0.4372 | 2045 | 0.3245 | - | +| 0.4382 | 2050 | 0.4148 | - | +| 0.4393 | 2055 | 0.3542 | - | +| 0.4404 | 2060 | 0.4493 | - | +| 0.4414 | 2065 | 0.376 | - | +| 0.4425 | 2070 | 0.322 | - | +| 0.4436 | 2075 | 0.3227 | - | +| 0.4446 | 2080 | 0.3046 | - 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| +| 1.9153 | 8960 | 0.2642 | - | +| 1.9164 | 8965 | 0.2782 | - | +| 1.9175 | 8970 | 0.2435 | - | +| 1.9186 | 8975 | 0.2492 | - | +| 1.9196 | 8980 | 0.2161 | - | +| 1.9207 | 8985 | 0.2549 | - | +| 1.9218 | 8990 | 0.2217 | - | +| 1.9228 | 8995 | 0.266 | - | +| 1.9239 | 9000 | 0.2909 | - | +| 1.9250 | 9005 | 0.2149 | - | +| 1.9260 | 9010 | 0.3099 | - | +| 1.9271 | 9015 | 0.1929 | - | +| 1.9282 | 9020 | 0.2854 | - | +| 1.9292 | 9025 | 0.1857 | - | +| 1.9303 | 9030 | 0.2445 | - | +| 1.9314 | 9035 | 0.226 | - | +| 1.9324 | 9040 | 0.2822 | - | +| 1.9335 | 9045 | 0.2541 | - | +| 1.9346 | 9050 | 0.2442 | - | +| 1.9357 | 9055 | 0.2504 | - | +| 1.9367 | 9060 | 0.2073 | - | +| 1.9378 | 9065 | 0.2245 | - | +| 1.9389 | 9070 | 0.2442 | - | +| 1.9399 | 9075 | 0.2382 | - | +| 1.9410 | 9080 | 0.2482 | - | +| 1.9421 | 9085 | 0.2849 | - | +| 1.9431 | 9090 | 0.2666 | - | +| 1.9442 | 9095 | 0.224 | - | +| 1.9453 | 9100 | 0.2961 | - | +| 1.9463 | 9105 | 0.217 | - | +| 1.9474 | 9110 | 0.2384 | - | +| 1.9485 | 9115 | 0.2205 | - | +| 1.9496 | 9120 | 0.2807 | - | +| 1.9506 | 9125 | 0.2577 | - | +| 1.9517 | 9130 | 0.3212 | - | +| 1.9528 | 9135 | 0.239 | - | +| 1.9538 | 9140 | 0.2283 | - | +| 1.9549 | 9145 | 0.2606 | - | +| 1.9560 | 9150 | 0.2684 | - | +| 1.9570 | 9155 | 0.2857 | - | +| 1.9581 | 9160 | 0.2962 | - | +| 1.9592 | 9165 | 0.2639 | - | +| 1.9602 | 9170 | 0.2549 | - | +| 1.9613 | 9175 | 0.2561 | - | +| 1.9624 | 9180 | 0.2324 | - | +| 1.9634 | 9185 | 0.3142 | - | +| 1.9645 | 9190 | 0.2778 | - | +| 1.9656 | 9195 | 0.2603 | - | +| 1.9667 | 9200 | 0.2191 | - | +| 1.9677 | 9205 | 0.2658 | - | +| 1.9688 | 9210 | 0.2701 | - | +| 1.9699 | 9215 | 0.3 | - | +| 1.9709 | 9220 | 0.2728 | - | +| 1.9720 | 9225 | 0.2724 | - | +| 1.9731 | 9230 | 0.1735 | - | +| 1.9741 | 9235 | 0.25 | - | +| 1.9752 | 9240 | 0.276 | - | +| 1.9763 | 9245 | 0.2581 | - | +| 1.9773 | 9250 | 0.2545 | - | +| 1.9784 | 9255 | 0.2251 | - | +| 1.9795 | 9260 | 0.269 | - | +| 1.9805 | 9265 | 0.2026 | - | +| 1.9816 | 9270 | 0.2297 | - | +| 1.9827 | 9275 | 0.2414 | - | +| 1.9838 | 9280 | 0.2724 | - | +| 1.9848 | 9285 | 0.2338 | - | +| 1.9859 | 9290 | 0.2606 | - | +| 1.9870 | 9295 | 0.2747 | - | +| 1.9880 | 9300 | 0.1792 | - | +| 1.9891 | 9305 | 0.2479 | - | +| 1.9902 | 9310 | 0.2554 | - | +| 1.9912 | 9315 | 0.25 | - | +| 1.9923 | 9320 | 0.2814 | - | +| 1.9934 | 9325 | 0.2102 | - | +| 1.9944 | 9330 | 0.2466 | - | +| 1.9955 | 9335 | 0.2547 | - | +| 1.9966 | 9340 | 0.2216 | - | +| 1.9976 | 9345 | 0.2521 | - | +| 1.9987 | 9350 | 0.2558 | - | +| 1.9998 | 9355 | 0.2345 | - | +| 2.0 | 9356 | - | 0.1219 | + +
+ +### Framework Versions +- Python: 3.11.13 +- Sentence Transformers: 5.1.1 +- PyLate: 1.3.4 +- Transformers: 4.56.2 +- PyTorch: 2.8.0+cu128 +- Accelerate: 1.12.0 +- Datasets: 4.4.1 +- Tokenizers: 0.22.1 + + +## 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" +} +``` + +#### PyLate +```bibtex +@misc{PyLate, +title={PyLate: Flexible Training and Retrieval for Late Interaction Models}, +author={Chaffin, Antoine and Sourty, Raphaƫl}, +url={https://github.com/lightonai/pylate}, +year={2024} +} +``` + + + + + + \ No newline at end of file