| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - dense |
| - generated_from_trainer |
| - dataset_size:4096000 |
| - loss:MultipleNegativesRankingLoss |
| base_model: BAAI/bge-small-en-v1.5 |
| widget: |
| - source_sentence: vascular disorders of the stomach |
| sentences: |
| - vascular disorders of the stomach, unspecified |
| - ringworm of the hand |
| - anogenital herpes |
| - source_sentence: neonatal malabsorption secondary to defect in carbohydrate metabolism |
| sentences: |
| - overfeeding problem of newborn |
| - other specified neonatal malabsorption syndromes |
| - bronchial asthma |
| - source_sentence: white matter disorder due to carbon monoxide toxicity |
| sentences: |
| - white matter disorder due to nitrous oxide toxicity |
| - neonatal dehydration |
| - moerman lethal chondrodysplasia |
| - source_sentence: chronic myelogenous leukaemia, bcr-abl1-positive in complete remission |
| sentences: |
| - chronic myeloid leukaemia, bcr-abl-positive in complete remission |
| - lesion of sciatic nerve |
| - tooth injury |
| - source_sentence: metabolic or transporter liver disease, unspecified |
| sentences: |
| - metabolic or transporter liver disease |
| - staphyloma of cornea |
| - spontaneous rupture of synovium or tendon, unspecified |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| metrics: |
| - cosine_accuracy@1 |
| - 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@10 |
| model-index: |
| - name: SentenceTransformer based on BAAI/bge-small-en-v1.5 |
| results: |
| - task: |
| type: information-retrieval |
| name: Information Retrieval |
| dataset: |
| name: icd11 val |
| type: icd11-val |
| metrics: |
| - type: cosine_accuracy@1 |
| value: 0.7478448275862069 |
| name: Cosine Accuracy@1 |
| - type: cosine_accuracy@5 |
| value: 0.8717672413793104 |
| name: Cosine Accuracy@5 |
| - type: cosine_accuracy@10 |
| value: 0.896551724137931 |
| name: Cosine Accuracy@10 |
| - type: cosine_precision@1 |
| value: 0.7478448275862069 |
| name: Cosine Precision@1 |
| - type: cosine_precision@3 |
| value: 0.6253591954022988 |
| name: Cosine Precision@3 |
| - type: cosine_precision@5 |
| value: 0.5258620689655173 |
| name: Cosine Precision@5 |
| - type: cosine_precision@10 |
| value: 0.3786637931034483 |
| name: Cosine Precision@10 |
| - type: cosine_recall@1 |
| value: 0.1588556265383415 |
| name: Cosine Recall@1 |
| - type: cosine_recall@3 |
| value: 0.37728305808648516 |
| name: Cosine Recall@3 |
| - type: cosine_recall@5 |
| value: 0.4796248471294223 |
| name: Cosine Recall@5 |
| - type: cosine_recall@10 |
| value: 0.6031417673743621 |
| name: Cosine Recall@10 |
| - type: cosine_ndcg@10 |
| value: 0.6649315033350626 |
| name: Cosine Ndcg@10 |
| - type: cosine_mrr@10 |
| value: 0.8006516830870286 |
| name: Cosine Mrr@10 |
| - type: cosine_map@10 |
| value: 0.5745241229374094 |
| name: Cosine Map@10 |
| --- |
| |
| # SentenceTransformer based on BAAI/bge-small-en-v1.5 |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more. |
|
|
| ## Model Details |
|
|
| ### Model Description |
| - **Model Type:** Sentence Transformer |
| - **Base model:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a --> |
| - **Maximum Sequence Length:** 30 tokens |
| - **Output Dimensionality:** 384 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Supported Modality:** Text |
| <!-- - **Training Dataset:** Unknown --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### Model Sources |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) |
| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
| ### Full Model Architecture |
|
|
| ``` |
| SentenceTransformer( |
| (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'}) |
| (1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'cls', '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("seanfarrell/icd11_ner_model_small") |
| # Run inference |
| queries = [ |
| 'metabolic or transporter liver disease, unspecified', |
| ] |
| documents = [ |
| 'metabolic or transporter liver disease', |
| 'spontaneous rupture of synovium or tendon, unspecified', |
| 'staphyloma of cornea', |
| ] |
| query_embeddings = model.encode_query(queries) |
| document_embeddings = model.encode_document(documents) |
| print(query_embeddings.shape, document_embeddings.shape) |
| # [1, 384] [3, 384] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(query_embeddings, document_embeddings) |
| print(similarities) |
| # tensor([[ 0.9840, -0.1296, -0.1292]]) |
| ``` |
| <!-- |
| ### 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 |
|
|
| * Dataset: `icd11-val` |
| * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.InformationRetrievalEvaluator) |
|
|
| | Metric | Value | |
| |:--------------------|:-----------| |
| | cosine_accuracy@1 | 0.7478 | |
| | cosine_accuracy@5 | 0.8718 | |
| | cosine_accuracy@10 | 0.8966 | |
| | cosine_precision@1 | 0.7478 | |
| | cosine_precision@3 | 0.6254 | |
| | cosine_precision@5 | 0.5259 | |
| | cosine_precision@10 | 0.3787 | |
| | cosine_recall@1 | 0.1589 | |
| | cosine_recall@3 | 0.3773 | |
| | cosine_recall@5 | 0.4796 | |
| | cosine_recall@10 | 0.6031 | |
| | **cosine_ndcg@10** | **0.6649** | |
| | cosine_mrr@10 | 0.8007 | |
| | cosine_map@10 | 0.5745 | |
| |
| <!-- |
| ## 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 |
| |
| #### Unnamed Dataset |
| |
| * Size: 4,096,000 training samples |
| * Columns: <code>anchor</code> and <code>positive</code> |
| * Approximate statistics based on the first 100 samples: |
| | | anchor | positive | |
| |:---------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
| | type | string | string | |
| | modality | text | text | |
| | details | <ul><li>min: 4 tokens</li><li>mean: 11.07 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.07 tokens</li><li>max: 28 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | |
| |:-------------------------------------------------------------|:-----------------------------------------------------------------| |
| | <code>duodenal adenoma in familial polyposis</code> | <code>benign duodenal neoplasm</code> | |
| | <code>congenital unilateral hip dislocation</code> | <code>developmental dysplasia of hip, unilateral</code> | |
| | <code>postprocedural pulmonary arterial tree disorder</code> | <code>postprocedural pulmonary arterial tree complication</code> | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim", |
| "gather_across_devices": false, |
| "directions": [ |
| "query_to_doc" |
| ], |
| "partition_mode": "joint", |
| "hardness_mode": null, |
| "hardness_strength": 0.0 |
| } |
| ``` |
| |
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
|
|
| - `per_device_train_batch_size`: 512 |
| - `num_train_epochs`: 1 |
| - `learning_rate`: 2e-05 |
| - `lr_scheduler_type`: cosine |
| - `warmup_steps`: 800 |
| - `weight_decay`: 0.01 |
| - `bf16`: True |
| - `per_device_eval_batch_size`: 256 |
| - `eval_on_start`: True |
| - `load_best_model_at_end`: True |
| - `dataloader_drop_last`: True |
|
|
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
|
|
| - `per_device_train_batch_size`: 512 |
| - `num_train_epochs`: 1 |
| - `max_steps`: -1 |
| - `learning_rate`: 2e-05 |
| - `lr_scheduler_type`: cosine |
| - `lr_scheduler_kwargs`: None |
| - `warmup_steps`: 800 |
| - `optim`: adamw_torch_fused |
| - `optim_args`: None |
| - `weight_decay`: 0.01 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `optim_target_modules`: None |
| - `gradient_accumulation_steps`: 1 |
| - `average_tokens_across_devices`: True |
| - `max_grad_norm`: 1.0 |
| - `label_smoothing_factor`: 0.0 |
| - `bf16`: True |
| - `fp16`: False |
| - `bf16_full_eval`: False |
| - `fp16_full_eval`: False |
| - `tf32`: None |
| - `gradient_checkpointing`: False |
| - `gradient_checkpointing_kwargs`: None |
| - `torch_compile`: False |
| - `torch_compile_backend`: None |
| - `torch_compile_mode`: None |
| - `use_liger_kernel`: False |
| - `liger_kernel_config`: None |
| - `use_cache`: False |
| - `neftune_noise_alpha`: None |
| - `torch_empty_cache_steps`: None |
| - `auto_find_batch_size`: False |
| - `log_on_each_node`: True |
| - `logging_nan_inf_filter`: True |
| - `include_num_input_tokens_seen`: no |
| - `log_level`: passive |
| - `log_level_replica`: warning |
| - `disable_tqdm`: False |
| - `project`: huggingface |
| - `trackio_space_id`: None |
| - `trackio_bucket_id`: None |
| - `trackio_static_space_id`: None |
| - `per_device_eval_batch_size`: 256 |
| - `prediction_loss_only`: True |
| - `eval_on_start`: True |
| - `eval_do_concat_batches`: True |
| - `eval_use_gather_object`: False |
| - `eval_accumulation_steps`: None |
| - `include_for_metrics`: [] |
| - `batch_eval_metrics`: False |
| - `save_only_model`: False |
| - `save_on_each_node`: False |
| - `enable_jit_checkpoint`: False |
| - `push_to_hub`: False |
| - `hub_private_repo`: None |
| - `hub_model_id`: None |
| - `hub_strategy`: every_save |
| - `hub_always_push`: False |
| - `hub_revision`: None |
| - `load_best_model_at_end`: True |
| - `ignore_data_skip`: False |
| - `restore_callback_states_from_checkpoint`: False |
| - `full_determinism`: False |
| - `seed`: 42 |
| - `data_seed`: None |
| - `use_cpu`: False |
| - `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 |
| - `dataloader_drop_last`: True |
| - `dataloader_num_workers`: 0 |
| - `dataloader_pin_memory`: True |
| - `dataloader_persistent_workers`: False |
| - `dataloader_prefetch_factor`: None |
| - `dataloader_multiprocessing_context`: None |
| - `dataloader_in_order`: True |
| - `remove_unused_columns`: True |
| - `label_names`: None |
| - `train_sampling_strategy`: random |
| - `length_column_name`: length |
| - `ddp_find_unused_parameters`: None |
| - `ddp_bucket_cap_mb`: None |
| - `ddp_broadcast_buffers`: False |
| - `ddp_static_graph`: None |
| - `ddp_backend`: None |
| - `ddp_timeout`: 1800 |
| - `fsdp`: None |
| - `fsdp_config`: None |
| - `deepspeed`: None |
| - `debug`: [] |
| - `skip_memory_metrics`: True |
| - `do_predict`: False |
| - `resume_from_checkpoint`: None |
| - `local_rank`: -1 |
| - `prompts`: None |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: proportional |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
| - `warmup_ratio`: None |
|
|
| </details> |
|
|
| ### Training Logs |
| <details><summary>Click to expand</summary> |
|
|
| | Epoch | Step | Training Loss | icd11-val_cosine_ndcg@10 | |
| |:----------:|:--------:|:-------------:|:------------------------:| |
| | 0 | 0 | - | 0.4871 | |
| | 0.0063 | 50 | 2.5057 | - | |
| | 0.0125 | 100 | 2.1921 | - | |
| | 0.0187 | 150 | 1.6707 | - | |
| | 0.025 | 200 | 1.4270 | - | |
| | 0.0312 | 250 | 1.3379 | - | |
| | 0.0375 | 300 | 1.2811 | - | |
| | 0.0437 | 350 | 1.2222 | - | |
| | 0.05 | 400 | 1.1848 | - | |
| | 0.0563 | 450 | 1.1484 | - | |
| | 0.0625 | 500 | 1.1260 | 0.5411 | |
| | 0.0688 | 550 | 1.0856 | - | |
| | 0.075 | 600 | 1.0654 | - | |
| | 0.0813 | 650 | 1.0247 | - | |
| | 0.0875 | 700 | 1.0050 | - | |
| | 0.0938 | 750 | 0.9669 | - | |
| | 0.1 | 800 | 0.9515 | - | |
| | 0.1062 | 850 | 0.9404 | - | |
| | 0.1125 | 900 | 0.8906 | - | |
| | 0.1187 | 950 | 0.8692 | - | |
| | 0.125 | 1000 | 0.8413 | 0.5746 | |
| | 0.1313 | 1050 | 0.8439 | - | |
| | 0.1375 | 1100 | 0.8270 | - | |
| | 0.1437 | 1150 | 0.8128 | - | |
| | 0.15 | 1200 | 0.7762 | - | |
| | 0.1562 | 1250 | 0.7666 | - | |
| | 0.1625 | 1300 | 0.7555 | - | |
| | 0.1688 | 1350 | 0.7562 | - | |
| | 0.175 | 1400 | 0.7180 | - | |
| | 0.1812 | 1450 | 0.6945 | - | |
| | 0.1875 | 1500 | 0.7032 | 0.6002 | |
| | 0.1938 | 1550 | 0.6747 | - | |
| | 0.2 | 1600 | 0.6729 | - | |
| | 0.2062 | 1650 | 0.6604 | - | |
| | 0.2125 | 1700 | 0.6644 | - | |
| | 0.2188 | 1750 | 0.6556 | - | |
| | 0.225 | 1800 | 0.6440 | - | |
| | 0.2313 | 1850 | 0.6284 | - | |
| | 0.2375 | 1900 | 0.6105 | - | |
| | 0.2437 | 1950 | 0.5935 | - | |
| | 0.25 | 2000 | 0.5923 | 0.6120 | |
| | 0.2562 | 2050 | 0.5850 | - | |
| | 0.2625 | 2100 | 0.5787 | - | |
| | 0.2687 | 2150 | 0.5764 | - | |
| | 0.275 | 2200 | 0.5643 | - | |
| | 0.2812 | 2250 | 0.5655 | - | |
| | 0.2875 | 2300 | 0.5463 | - | |
| | 0.2938 | 2350 | 0.5414 | - | |
| | 0.3 | 2400 | 0.5368 | - | |
| | 0.3063 | 2450 | 0.5236 | - | |
| | 0.3125 | 2500 | 0.5109 | 0.6230 | |
| | 0.3187 | 2550 | 0.5308 | - | |
| | 0.325 | 2600 | 0.5000 | - | |
| | 0.3312 | 2650 | 0.5069 | - | |
| | 0.3375 | 2700 | 0.5120 | - | |
| | 0.3438 | 2750 | 0.4853 | - | |
| | 0.35 | 2800 | 0.4877 | - | |
| | 0.3563 | 2850 | 0.4786 | - | |
| | 0.3625 | 2900 | 0.4869 | - | |
| | 0.3688 | 2950 | 0.4865 | - | |
| | 0.375 | 3000 | 0.4561 | 0.6332 | |
| | 0.3812 | 3050 | 0.4640 | - | |
| | 0.3875 | 3100 | 0.4629 | - | |
| | 0.3937 | 3150 | 0.4575 | - | |
| | 0.4 | 3200 | 0.4493 | - | |
| | 0.4062 | 3250 | 0.4503 | - | |
| | 0.4125 | 3300 | 0.4301 | - | |
| | 0.4188 | 3350 | 0.4410 | - | |
| | 0.425 | 3400 | 0.4415 | - | |
| | 0.4313 | 3450 | 0.4316 | - | |
| | 0.4375 | 3500 | 0.4359 | 0.6441 | |
| | 0.4437 | 3550 | 0.4147 | - | |
| | 0.45 | 3600 | 0.4028 | - | |
| | 0.4562 | 3650 | 0.4002 | - | |
| | 0.4625 | 3700 | 0.4132 | - | |
| | 0.4688 | 3750 | 0.4021 | - | |
| | 0.475 | 3800 | 0.4058 | - | |
| | 0.4813 | 3850 | 0.4023 | - | |
| | 0.4875 | 3900 | 0.4038 | - | |
| | 0.4938 | 3950 | 0.4007 | - | |
| | 0.5 | 4000 | 0.3894 | 0.6501 | |
| | 0.5062 | 4050 | 0.3856 | - | |
| | 0.5125 | 4100 | 0.3879 | - | |
| | 0.5188 | 4150 | 0.3758 | - | |
| | 0.525 | 4200 | 0.3796 | - | |
| | 0.5312 | 4250 | 0.3890 | - | |
| | 0.5375 | 4300 | 0.3831 | - | |
| | 0.5437 | 4350 | 0.3806 | - | |
| | 0.55 | 4400 | 0.3781 | - | |
| | 0.5563 | 4450 | 0.3761 | - | |
| | 0.5625 | 4500 | 0.3723 | 0.6551 | |
| | 0.5687 | 4550 | 0.3603 | - | |
| | 0.575 | 4600 | 0.3595 | - | |
| | 0.5813 | 4650 | 0.3524 | - | |
| | 0.5875 | 4700 | 0.3695 | - | |
| | 0.5938 | 4750 | 0.3552 | - | |
| | 0.6 | 4800 | 0.3521 | - | |
| | 0.6062 | 4850 | 0.3459 | - | |
| | 0.6125 | 4900 | 0.3472 | - | |
| | 0.6188 | 4950 | 0.3511 | - | |
| | 0.625 | 5000 | 0.3537 | 0.6589 | |
| | 0.6312 | 5050 | 0.3490 | - | |
| | 0.6375 | 5100 | 0.3428 | - | |
| | 0.6438 | 5150 | 0.3492 | - | |
| | 0.65 | 5200 | 0.3471 | - | |
| | 0.6562 | 5250 | 0.3348 | - | |
| | 0.6625 | 5300 | 0.3404 | - | |
| | 0.6687 | 5350 | 0.3442 | - | |
| | 0.675 | 5400 | 0.3494 | - | |
| | 0.6813 | 5450 | 0.3306 | - | |
| | 0.6875 | 5500 | 0.3349 | 0.6598 | |
| | 0.6937 | 5550 | 0.3401 | - | |
| | 0.7 | 5600 | 0.3374 | - | |
| | 0.7063 | 5650 | 0.3306 | - | |
| | 0.7125 | 5700 | 0.3277 | - | |
| | 0.7188 | 5750 | 0.3274 | - | |
| | 0.725 | 5800 | 0.3318 | - | |
| | 0.7312 | 5850 | 0.3316 | - | |
| | 0.7375 | 5900 | 0.3273 | - | |
| | 0.7438 | 5950 | 0.3252 | - | |
| | 0.75 | 6000 | 0.3306 | 0.6636 | |
| | 0.7562 | 6050 | 0.3347 | - | |
| | 0.7625 | 6100 | 0.3186 | - | |
| | 0.7688 | 6150 | 0.3301 | - | |
| | 0.775 | 6200 | 0.3203 | - | |
| | 0.7812 | 6250 | 0.3234 | - | |
| | 0.7875 | 6300 | 0.3134 | - | |
| | 0.7937 | 6350 | 0.3175 | - | |
| | 0.8 | 6400 | 0.3312 | - | |
| | 0.8063 | 6450 | 0.3302 | - | |
| | 0.8125 | 6500 | 0.3189 | 0.6644 | |
| | 0.8187 | 6550 | 0.3191 | - | |
| | 0.825 | 6600 | 0.3265 | - | |
| | 0.8313 | 6650 | 0.3192 | - | |
| | 0.8375 | 6700 | 0.3111 | - | |
| | 0.8438 | 6750 | 0.3124 | - | |
| | 0.85 | 6800 | 0.3101 | - | |
| | 0.8562 | 6850 | 0.3188 | - | |
| | 0.8625 | 6900 | 0.3180 | - | |
| | 0.8688 | 6950 | 0.3153 | - | |
| | 0.875 | 7000 | 0.3159 | 0.6646 | |
| | 0.8812 | 7050 | 0.3132 | - | |
| | 0.8875 | 7100 | 0.3168 | - | |
| | 0.8938 | 7150 | 0.3227 | - | |
| | 0.9 | 7200 | 0.3121 | - | |
| | 0.9062 | 7250 | 0.3212 | - | |
| | 0.9125 | 7300 | 0.3195 | - | |
| | 0.9187 | 7350 | 0.3177 | - | |
| | 0.925 | 7400 | 0.3228 | - | |
| | 0.9313 | 7450 | 0.3172 | - | |
| | **0.9375** | **7500** | **0.3045** | **0.6652** | |
| | 0.9437 | 7550 | 0.3052 | - | |
| | 0.95 | 7600 | 0.3273 | - | |
| | 0.9563 | 7650 | 0.3163 | - | |
| | 0.9625 | 7700 | 0.3138 | - | |
| | 0.9688 | 7750 | 0.3280 | - | |
| | 0.975 | 7800 | 0.3074 | - | |
| | 0.9812 | 7850 | 0.3266 | - | |
| | 0.9875 | 7900 | 0.3214 | - | |
| | 0.9938 | 7950 | 0.3240 | - | |
| | 1.0 | 8000 | 0.3141 | 0.6649 | |
|
|
| * The bold row denotes the saved checkpoint. |
| </details> |
|
|
| ### Training Time |
| - **Training**: 8.9 minutes |
| - **Evaluation**: 45.4 seconds |
| - **Total**: 9.7 minutes |
|
|
| ### Framework Versions |
| - Python: 3.13.15 |
| - Sentence Transformers: 5.7.0 |
| - Transformers: 5.18.0 |
| - PyTorch: 2.11.0+cu130 |
| - Accelerate: 1.15.0 |
| - Datasets: 4.8.5 |
| - Tokenizers: 0.23.2 |
|
|
| ## Additional Resources |
|
|
| - [Training and Finetuning Embedding Models with Sentence Transformers](https://huggingface.co/blog/train-sentence-transformers): the end-to-end guide for training or finetuning Sentence Transformer models. |
| - [Introduction to Matryoshka Embedding Models](https://huggingface.co/blog/matryoshka): variable-size embeddings that can be truncated with minimal quality loss. |
| - [Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval](https://huggingface.co/blog/embedding-quantization): post-training compression of embedding vectors. |
| - [Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/multimodal-sentence-transformers): use text, image, audio, and video models through the same API. |
| - [Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/train-multimodal-sentence-transformers): train multimodal embedding models, with a Visual Document Retrieval walkthrough. |
|
|
| ## 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", |
| } |
| ``` |
|
|
| #### MultipleNegativesRankingLoss |
| ```bibtex |
| @misc{oord2019representationlearningcontrastivepredictive, |
| title={Representation Learning with Contrastive Predictive Coding}, |
| author={Aaron van den Oord and Yazhe Li and Oriol Vinyals}, |
| year={2019}, |
| eprint={1807.03748}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.LG}, |
| url={https://arxiv.org/abs/1807.03748}, |
| } |
| ``` |
|
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