| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:1554 |
| - loss:ContrastiveLoss |
| base_model: sentence-transformers/all-MiniLM-L6-v2 |
| widget: |
| - source_sentence: 'Patient reports increased joint pain, swelling, and morning stiffness |
| associated with Rheumatoid Arthritis. |
| |
| The patient describes worsening joint pain in the hands and knees, along with |
| increased swelling and morning stiffness lasting over 60 minutes, over the past |
| month. |
| |
| Musculoskeletal: Increased joint pain, swelling, morning stiffness. |
| |
| Rheumatoid Arthritis, managed with disease-modifying antirheumatic drugs (DMARDs). |
| |
| Worsening symptoms in a patient with Rheumatoid Arthritis.' |
| sentences: |
| - Chronic Obstructive Pulmonary Disease With (Acute) Exacerbation |
| - Enteroviral Vesicular Stomatitis With Exanthem |
| - Unspecified Juvenile Rheumatoid Arthritis Of Unspecified Site |
| - source_sentence: 'Follow-up for well-controlled type 2 diabetes mellitus. |
| |
| Type 2 Diabetes Mellitus (ICD-10 code: E11.9) |
| |
| Well-controlled diabetes. |
| |
| Adjust diabetes medication. |
| |
| Order HbA1c levels. |
| |
| Take adjusted medication as directed. |
| |
| Monitor blood glucose regularly.' |
| sentences: |
| - Type 2 Diabetes Mellitus Without Complications |
| - Type 2 diabetes mellitus without complications |
| - Diverticulitis Of Intestine, Part Unspecified, Without Perforation Or Abscess |
| Without Bleeding |
| - source_sentence: 'A 55-year-old female patient with a history of hypertension presented |
| to the clinic for a follow-up appointment. |
| |
| Her blood pressure has been poorly controlled despite taking two different antihypertensive |
| medications. |
| |
| Past Medical History (PMH): Hypertension |
| |
| Vital signs: Temperature 98.6°F, heart rate 90 beats per minute, respiratory rate |
| 18 breaths per minute, blood pressure 165/95 mmHg |
| |
| Assessment: Uncontrolled hypertension |
| |
| Plan: Review adherence to medication regimen |
| |
| Plan: Adjust medication dosages or switch to different medications |
| |
| Plan: Reinforce lifestyle modification recommendations |
| |
| Chief Complaint: Poorly controlled hypertension' |
| sentences: |
| - Hypothyroidism, Unspecified |
| - Hyperlipidemia, Unspecified |
| - Essential (Primary) Hypertension |
| - source_sentence: 'Presenting with fatigue, weight gain, and cold intolerance over |
| the past few months. |
| |
| Positive for constipation, dry skin, and brittle nails. |
| |
| Thyroid enlargement (goiter). |
| |
| Diminished reflexes. |
| |
| Hypothyroidism |
| |
| Initiate levothyroxine therapy. |
| |
| Schedule follow-up for thyroid function monitoring.' |
| sentences: |
| - Abnormal Weight Loss |
| - Acute Upper Respiratory Infection, Unspecified |
| - Hypothyroidism, Unspecified |
| - source_sentence: 'A 45-year-old male patient presented to the emergency department |
| with a chief complaint of severe lower back pain. |
| |
| He reports that the pain started suddenly about two hours ago while lifting a |
| heavy object. |
| |
| The pain is located in the lower back, radiates down the right leg, and is described |
| as sharp and stabbing. |
| |
| He also reports numbness and tingling in the right leg. |
| |
| Musculoskeletal: Severe lower back pain radiating down the right leg, numbness |
| and tingling in the right leg |
| |
| Musculoskeletal: Limited range of motion in the lumbar spine due to pain. |
| |
| Muscle guarding is present in the paraspinal muscles. |
| |
| Motor strength and sensation are decreased in the right leg compared to the left. |
| |
| Suspected lumbar spine herniated disc |
| |
| X-rays of the lumbar spine to confirm the diagnosis |
| |
| MRI of the lumbar spine if X-rays are inconclusive |
| |
| Neurological consultation for further evaluation and management |
| |
| Pain management with medication and physical therapy' |
| sentences: |
| - Postconcussional Syndrome |
| - Other Intervertebral Disc Displacement, Lumbar Region |
| - Encounter For Immunization |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| --- |
| |
| # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf --> |
| - **Maximum Sequence Length:** 256 tokens |
| - **Output Dimensionality:** 384 dimensions |
| - **Similarity Function:** Cosine Similarity |
| <!-- - **Training Dataset:** Unknown --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### Model Sources |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
| ### Full Model Architecture |
|
|
| ``` |
| SentenceTransformer( |
| (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel |
| (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
| (2): Normalize() |
| ) |
| ``` |
|
|
| ## Usage |
|
|
| ### Direct Usage (Sentence Transformers) |
|
|
| First install the Sentence Transformers library: |
|
|
| ```bash |
| pip install -U sentence-transformers |
| ``` |
|
|
| Then you can load this model and run inference. |
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| # Download from the 🤗 Hub |
| model = SentenceTransformer("sentence_transformers_model_id") |
| # Run inference |
| sentences = [ |
| 'A 45-year-old male patient presented to the emergency department with a chief complaint of severe lower back pain.\nHe reports that the pain started suddenly about two hours ago while lifting a heavy object.\nThe pain is located in the lower back, radiates down the right leg, and is described as sharp and stabbing.\nHe also reports numbness and tingling in the right leg.\nMusculoskeletal: Severe lower back pain radiating down the right leg, numbness and tingling in the right leg\nMusculoskeletal: Limited range of motion in the lumbar spine due to pain.\nMuscle guarding is present in the paraspinal muscles.\nMotor strength and sensation are decreased in the right leg compared to the left.\nSuspected lumbar spine herniated disc\nX-rays of the lumbar spine to confirm the diagnosis\nMRI of the lumbar spine if X-rays are inconclusive\nNeurological consultation for further evaluation and management\nPain management with medication and physical therapy', |
| 'Other Intervertebral Disc Displacement, Lumbar Region', |
| 'Postconcussional Syndrome', |
| ] |
| embeddings = model.encode(sentences) |
| print(embeddings.shape) |
| # [3, 384] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(embeddings, embeddings) |
| print(similarities.shape) |
| # [3, 3] |
| ``` |
|
|
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| <!-- |
| ## 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: 1,554 training samples |
| * Columns: <code>anchor</code>, <code>positive</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | label | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------| |
| | type | string | string | float | |
| | details | <ul><li>min: 3 tokens</li><li>mean: 83.3 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.12 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | anchor | positive | label | |
| |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------| |
| | <code>Worsening sciatic pain despite conservative treatment<br>She reports that her sciatic pain has been worsening despite conservative treatment with rest, ice, over-the-counter pain relievers, and lumbar spine exercises.<br>She says that the pain is now radiating down her entire leg, from her lower back to her foot.<br>She also reports that she has numbness and tingling in her toes.<br>The pain is worse when she sits or stands for long periods of time.<br>Musculoskeletal: Worsening sciatic pain radiating down the entire leg<br>Neurological: Numbness and tingling in the toes of the affected leg<br>Exacerbation of sciatica</code> | <code>Sciatica, Unspecified Side</code> | <code>1.0</code> | |
| | <code>Chief Complaint: Malaria<br>Patient presents with fever, chills, and headache.<br>Reports a sudden onset of symptoms after returning from a recent trip to a malaria-endemic region.<br>Positive for fever and chills.<br>Recent travel to a malaria-endemic area.<br>Fever (38.5°C), chills, and generalized body aches.<br>High suspicion for malaria infection.<br>Initiate prompt diagnostic testing and antimalarial treatment.<br>Blood smear for malaria parasites.<br>Prescribe antimalarial medications based on species identification.<br>Consideration of hospitalization if severe symptoms or complications arise.<br>Follow-up in two weeks for reassessment and treatment response.<br>Blood smear for Plasmodium species identification.<br>Complete blood count (CBC) for assessment of anemia and thrombocytopenia.<br>Liver function tests to assess for malaria-related complications.</code> | <code>Unspecified Malaria</code> | <code>1.0</code> | |
| | <code>The patient returns for a follow-up visit after being diagnosed with psychosis.<br>He reports partial improvement in symptoms with medication but notes persistent paranoia and difficulty concentrating.<br>Partial improvement in sleep but persistent paranoia.<br>Some reduction in auditory hallucinations, persistent racing thoughts.<br>Ongoing diagnosis of psychosis and mood disorder.<br>Observable signs of residual psychosis and disorganized thoughts.<br>Partial improvement in psychosis.<br>Adjust medication dosage or consider alternative medications.<br>Continue individual therapy for ongoing management.<br>Monitor for side effects and therapeutic response.<br>Attend scheduled therapy sessions.<br>Take prescribed medications as directed.<br>Report any new or worsening symptoms promptly.</code> | <code>Psychotic Disorder With Delusions Due To Known Physiological Condition</code> | <code>1.0</code> | |
| * Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters: |
| ```json |
| { |
| "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", |
| "margin": 0.5, |
| "size_average": true |
| } |
| ``` |
|
|
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
|
|
| - `per_device_train_batch_size`: 16 |
| - `per_device_eval_batch_size`: 16 |
| - `learning_rate`: 2e-05 |
| - `num_train_epochs`: 5 |
| - `warmup_ratio`: 0.1 |
|
|
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
|
|
| - `overwrite_output_dir`: False |
| - `do_predict`: False |
| - `eval_strategy`: no |
| - `prediction_loss_only`: True |
| - `per_device_train_batch_size`: 16 |
| - `per_device_eval_batch_size`: 16 |
| - `per_gpu_train_batch_size`: None |
| - `per_gpu_eval_batch_size`: None |
| - `gradient_accumulation_steps`: 1 |
| - `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`: 5 |
| - `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`: False |
| - `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`: 0 |
| - `dataloader_prefetch_factor`: None |
| - `past_index`: -1 |
| - `disable_tqdm`: False |
| - `remove_unused_columns`: True |
| - `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} |
| - `deepspeed`: None |
| - `label_smoothing_factor`: 0.0 |
| - `optim`: adamw_torch |
| - `optim_args`: None |
| - `adafactor`: False |
| - `group_by_length`: False |
| - `length_column_name`: length |
| - `ddp_find_unused_parameters`: None |
| - `ddp_bucket_cap_mb`: None |
| - `ddp_broadcast_buffers`: False |
| - `dataloader_pin_memory`: True |
| - `dataloader_persistent_workers`: False |
| - `skip_memory_metrics`: True |
| - `use_legacy_prediction_loop`: False |
| - `push_to_hub`: False |
| - `resume_from_checkpoint`: None |
| - `hub_model_id`: None |
| - `hub_strategy`: every_save |
| - `hub_private_repo`: None |
| - `hub_always_push`: False |
| - `gradient_checkpointing`: False |
| - `gradient_checkpointing_kwargs`: None |
| - `include_inputs_for_metrics`: False |
| - `include_for_metrics`: [] |
| - `eval_do_concat_batches`: True |
| - `fp16_backend`: auto |
| - `push_to_hub_model_id`: None |
| - `push_to_hub_organization`: None |
| - `mp_parameters`: |
| - `auto_find_batch_size`: False |
| - `full_determinism`: False |
| - `torchdynamo`: None |
| - `ray_scope`: last |
| - `ddp_timeout`: 1800 |
| - `torch_compile`: False |
| - `torch_compile_backend`: None |
| - `torch_compile_mode`: None |
| - `include_tokens_per_second`: False |
| - `include_num_input_tokens_seen`: False |
| - `neftune_noise_alpha`: None |
| - `optim_target_modules`: None |
| - `batch_eval_metrics`: False |
| - `eval_on_start`: False |
| - `use_liger_kernel`: False |
| - `eval_use_gather_object`: False |
| - `average_tokens_across_devices`: False |
| - `prompts`: None |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: proportional |
|
|
| </details> |
|
|
| ### Training Logs |
| <details><summary>Click to expand</summary> |
|
|
| | Epoch | Step | Training Loss | |
| |:------:|:----:|:-------------:| |
| | 0.0102 | 1 | 0.1624 | |
| | 0.0204 | 2 | 0.1369 | |
| | 0.0306 | 3 | 0.1151 | |
| | 0.0408 | 4 | 0.1031 | |
| | 0.0510 | 5 | 0.097 | |
| | 0.0612 | 6 | 0.095 | |
| | 0.0714 | 7 | 0.1236 | |
| | 0.0816 | 8 | 0.1177 | |
| | 0.0918 | 9 | 0.0931 | |
| | 0.1020 | 10 | 0.1049 | |
| | 0.1122 | 11 | 0.0757 | |
| | 0.1224 | 12 | 0.0936 | |
| | 0.1327 | 13 | 0.0797 | |
| | 0.1429 | 14 | 0.0855 | |
| | 0.1531 | 15 | 0.079 | |
| | 0.1633 | 16 | 0.0666 | |
| | 0.1735 | 17 | 0.073 | |
| | 0.1837 | 18 | 0.0669 | |
| | 0.1939 | 19 | 0.0517 | |
| | 0.2041 | 20 | 0.0667 | |
| | 0.2143 | 21 | 0.0639 | |
| | 0.2245 | 22 | 0.0729 | |
| | 0.2347 | 23 | 0.0565 | |
| | 0.2449 | 24 | 0.0501 | |
| | 0.2551 | 25 | 0.0596 | |
| | 0.2653 | 26 | 0.0478 | |
| | 0.2755 | 27 | 0.0306 | |
| | 0.2857 | 28 | 0.0509 | |
| | 0.2959 | 29 | 0.0415 | |
| | 0.3061 | 30 | 0.0396 | |
| | 0.3163 | 31 | 0.0215 | |
| | 0.3265 | 32 | 0.0402 | |
| | 0.3367 | 33 | 0.0692 | |
| | 0.3469 | 34 | 0.0602 | |
| | 0.3571 | 35 | 0.0215 | |
| | 0.3673 | 36 | 0.0274 | |
| | 0.3776 | 37 | 0.0212 | |
| | 0.3878 | 38 | 0.0231 | |
| | 0.3980 | 39 | 0.0159 | |
| | 0.4082 | 40 | 0.0154 | |
| | 0.4184 | 41 | 0.013 | |
| | 0.4286 | 42 | 0.0144 | |
| | 0.4388 | 43 | 0.0353 | |
| | 0.4490 | 44 | 0.0169 | |
| | 0.4592 | 45 | 0.0055 | |
| | 0.4694 | 46 | 0.0098 | |
| | 0.4796 | 47 | 0.0071 | |
| | 0.4898 | 48 | 0.0167 | |
| | 0.5 | 49 | 0.0062 | |
| | 0.5102 | 50 | 0.0064 | |
| | 0.5204 | 51 | 0.0125 | |
| | 0.5306 | 52 | 0.0044 | |
| | 0.5408 | 53 | 0.0193 | |
| | 0.5510 | 54 | 0.0058 | |
| | 0.5612 | 55 | 0.0043 | |
| | 0.5714 | 56 | 0.0036 | |
| | 0.5816 | 57 | 0.0018 | |
| | 0.5918 | 58 | 0.0039 | |
| | 0.6020 | 59 | 0.0031 | |
| | 0.6122 | 60 | 0.0019 | |
| | 0.6224 | 61 | 0.003 | |
| | 0.6327 | 62 | 0.003 | |
| | 0.6429 | 63 | 0.0039 | |
| | 0.6531 | 64 | 0.0048 | |
| | 0.6633 | 65 | 0.0013 | |
| | 0.6735 | 66 | 0.0039 | |
| | 0.6837 | 67 | 0.0113 | |
| | 0.6939 | 68 | 0.0042 | |
| | 0.7041 | 69 | 0.0029 | |
| | 0.7143 | 70 | 0.0014 | |
| | 0.7245 | 71 | 0.0012 | |
| | 0.7347 | 72 | 0.001 | |
| | 0.7449 | 73 | 0.0128 | |
| | 0.7551 | 74 | 0.0076 | |
| | 0.7653 | 75 | 0.0031 | |
| | 0.7755 | 76 | 0.0012 | |
| | 0.7857 | 77 | 0.0014 | |
| | 0.7959 | 78 | 0.0015 | |
| | 0.8061 | 79 | 0.0017 | |
| | 0.8163 | 80 | 0.0014 | |
| | 0.8265 | 81 | 0.0015 | |
| | 0.8367 | 82 | 0.0013 | |
| | 0.8469 | 83 | 0.001 | |
| | 0.8571 | 84 | 0.0021 | |
| | 0.8673 | 85 | 0.0008 | |
| | 0.8776 | 86 | 0.0009 | |
| | 0.8878 | 87 | 0.0117 | |
| | 0.8980 | 88 | 0.003 | |
| | 0.9082 | 89 | 0.0008 | |
| | 0.9184 | 90 | 0.0068 | |
| | 0.9286 | 91 | 0.0014 | |
| | 0.9388 | 92 | 0.0014 | |
| | 0.9490 | 93 | 0.0007 | |
| | 0.9592 | 94 | 0.0011 | |
| | 0.9694 | 95 | 0.0009 | |
| | 0.9796 | 96 | 0.0008 | |
| | 0.9898 | 97 | 0.0011 | |
| | 1.0 | 98 | 0.0011 | |
| | 1.0102 | 99 | 0.0005 | |
| | 1.0204 | 100 | 0.0005 | |
| | 1.0306 | 101 | 0.0012 | |
| | 1.0408 | 102 | 0.0008 | |
| | 1.0510 | 103 | 0.0016 | |
| | 1.0612 | 104 | 0.0005 | |
| | 1.0714 | 105 | 0.0015 | |
| | 1.0816 | 106 | 0.0005 | |
| | 1.0918 | 107 | 0.0018 | |
| | 1.1020 | 108 | 0.0006 | |
| | 1.1122 | 109 | 0.0006 | |
| | 1.1224 | 110 | 0.0043 | |
| | 1.1327 | 111 | 0.0007 | |
| | 1.1429 | 112 | 0.0009 | |
| | 1.1531 | 113 | 0.0007 | |
| | 1.1633 | 114 | 0.0019 | |
| | 1.1735 | 115 | 0.0032 | |
| | 1.1837 | 116 | 0.0004 | |
| | 1.1939 | 117 | 0.0005 | |
| | 1.2041 | 118 | 0.0005 | |
| | 1.2143 | 119 | 0.0009 | |
| | 1.2245 | 120 | 0.0018 | |
| | 1.2347 | 121 | 0.0006 | |
| | 1.2449 | 122 | 0.0004 | |
| | 1.2551 | 123 | 0.0004 | |
| | 1.2653 | 124 | 0.0008 | |
| | 1.2755 | 125 | 0.0007 | |
| | 1.2857 | 126 | 0.0006 | |
| | 1.2959 | 127 | 0.0004 | |
| | 1.3061 | 128 | 0.0032 | |
| | 1.3163 | 129 | 0.0011 | |
| | 1.3265 | 130 | 0.0008 | |
| | 1.3367 | 131 | 0.0006 | |
| | 1.3469 | 132 | 0.0004 | |
| | 1.3571 | 133 | 0.0005 | |
| | 1.3673 | 134 | 0.0003 | |
| | 1.3776 | 135 | 0.0006 | |
| | 1.3878 | 136 | 0.0009 | |
| | 1.3980 | 137 | 0.0003 | |
| | 1.4082 | 138 | 0.0003 | |
| | 1.4184 | 139 | 0.0005 | |
| | 1.4286 | 140 | 0.0005 | |
| | 1.4388 | 141 | 0.0005 | |
| | 1.4490 | 142 | 0.0006 | |
| | 1.4592 | 143 | 0.0022 | |
| | 1.4694 | 144 | 0.0004 | |
| | 1.4796 | 145 | 0.0012 | |
| | 1.4898 | 146 | 0.0006 | |
| | 1.5 | 147 | 0.0003 | |
| | 1.5102 | 148 | 0.0008 | |
| | 1.5204 | 149 | 0.0004 | |
| | 1.5306 | 150 | 0.0004 | |
| | 1.5408 | 151 | 0.0004 | |
| | 1.5510 | 152 | 0.0004 | |
| | 1.5612 | 153 | 0.0007 | |
| | 1.5714 | 154 | 0.0022 | |
| | 1.5816 | 155 | 0.0005 | |
| | 1.5918 | 156 | 0.0003 | |
| | 1.6020 | 157 | 0.0005 | |
| | 1.6122 | 158 | 0.0003 | |
| | 1.6224 | 159 | 0.0004 | |
| | 1.6327 | 160 | 0.0004 | |
| | 1.6429 | 161 | 0.0002 | |
| | 1.6531 | 162 | 0.0005 | |
| | 1.6633 | 163 | 0.0005 | |
| | 1.6735 | 164 | 0.0003 | |
| | 1.6837 | 165 | 0.0005 | |
| | 1.6939 | 166 | 0.0005 | |
| | 1.7041 | 167 | 0.0004 | |
| | 1.7143 | 168 | 0.0003 | |
| | 1.7245 | 169 | 0.0003 | |
| | 1.7347 | 170 | 0.0003 | |
| | 1.7449 | 171 | 0.0005 | |
| | 1.7551 | 172 | 0.0005 | |
| | 1.7653 | 173 | 0.0002 | |
| | 1.7755 | 174 | 0.0005 | |
| | 1.7857 | 175 | 0.0003 | |
| | 1.7959 | 176 | 0.0006 | |
| | 1.8061 | 177 | 0.0003 | |
| | 1.8163 | 178 | 0.0004 | |
| | 1.8265 | 179 | 0.0004 | |
| | 1.8367 | 180 | 0.0002 | |
| | 1.8469 | 181 | 0.0002 | |
| | 1.8571 | 182 | 0.0005 | |
| | 1.8673 | 183 | 0.0003 | |
| | 1.8776 | 184 | 0.0003 | |
| | 1.8878 | 185 | 0.0002 | |
| | 1.8980 | 186 | 0.0003 | |
| | 1.9082 | 187 | 0.0032 | |
| | 1.9184 | 188 | 0.0006 | |
| | 1.9286 | 189 | 0.0003 | |
| | 1.9388 | 190 | 0.0005 | |
| | 1.9490 | 191 | 0.0003 | |
| | 1.9592 | 192 | 0.0004 | |
| | 1.9694 | 193 | 0.0004 | |
| | 1.9796 | 194 | 0.0004 | |
| | 1.9898 | 195 | 0.0003 | |
| | 2.0 | 196 | 0.0001 | |
| | 2.0102 | 197 | 0.0003 | |
| | 2.0204 | 198 | 0.0003 | |
| | 2.0306 | 199 | 0.0002 | |
| | 2.0408 | 200 | 0.0002 | |
| | 2.0510 | 201 | 0.0003 | |
| | 2.0612 | 202 | 0.0002 | |
| | 2.0714 | 203 | 0.0002 | |
| | 2.0816 | 204 | 0.0003 | |
| | 2.0918 | 205 | 0.0003 | |
| | 2.1020 | 206 | 0.0008 | |
| | 2.1122 | 207 | 0.0004 | |
| | 2.1224 | 208 | 0.0004 | |
| | 2.1327 | 209 | 0.0004 | |
| | 2.1429 | 210 | 0.0003 | |
| | 2.1531 | 211 | 0.0004 | |
| | 2.1633 | 212 | 0.0002 | |
| | 2.1735 | 213 | 0.0002 | |
| | 2.1837 | 214 | 0.0002 | |
| | 2.1939 | 215 | 0.0002 | |
| | 2.2041 | 216 | 0.0002 | |
| | 2.2143 | 217 | 0.0003 | |
| | 2.2245 | 218 | 0.0004 | |
| | 2.2347 | 219 | 0.0003 | |
| | 2.2449 | 220 | 0.0002 | |
| | 2.2551 | 221 | 0.0002 | |
| | 2.2653 | 222 | 0.0003 | |
| | 2.2755 | 223 | 0.0002 | |
| | 2.2857 | 224 | 0.0003 | |
| | 2.2959 | 225 | 0.0002 | |
| | 2.3061 | 226 | 0.0003 | |
| | 2.3163 | 227 | 0.0002 | |
| | 2.3265 | 228 | 0.0004 | |
| | 2.3367 | 229 | 0.0002 | |
| | 2.3469 | 230 | 0.0002 | |
| | 2.3571 | 231 | 0.001 | |
| | 2.3673 | 232 | 0.0002 | |
| | 2.3776 | 233 | 0.0006 | |
| | 2.3878 | 234 | 0.0003 | |
| | 2.3980 | 235 | 0.0003 | |
| | 2.4082 | 236 | 0.0005 | |
| | 2.4184 | 237 | 0.0004 | |
| | 2.4286 | 238 | 0.0011 | |
| | 2.4388 | 239 | 0.0009 | |
| | 2.4490 | 240 | 0.0004 | |
| | 2.4592 | 241 | 0.0003 | |
| | 2.4694 | 242 | 0.0003 | |
| | 2.4796 | 243 | 0.0002 | |
| | 2.4898 | 244 | 0.0004 | |
| | 2.5 | 245 | 0.0002 | |
| | 2.5102 | 246 | 0.0002 | |
| | 2.5204 | 247 | 0.0004 | |
| | 2.5306 | 248 | 0.0003 | |
| | 2.5408 | 249 | 0.0002 | |
| | 2.5510 | 250 | 0.0006 | |
| | 2.5612 | 251 | 0.0002 | |
| | 2.5714 | 252 | 0.0002 | |
| | 2.5816 | 253 | 0.0002 | |
| | 2.5918 | 254 | 0.0002 | |
| | 2.6020 | 255 | 0.0013 | |
| | 2.6122 | 256 | 0.0002 | |
| | 2.6224 | 257 | 0.0012 | |
| | 2.6327 | 258 | 0.0003 | |
| | 2.6429 | 259 | 0.0002 | |
| | 2.6531 | 260 | 0.0003 | |
| | 2.6633 | 261 | 0.0002 | |
| | 2.6735 | 262 | 0.0011 | |
| | 2.6837 | 263 | 0.0003 | |
| | 2.6939 | 264 | 0.0003 | |
| | 2.7041 | 265 | 0.0004 | |
| | 2.7143 | 266 | 0.0003 | |
| | 2.7245 | 267 | 0.0001 | |
| | 2.7347 | 268 | 0.0002 | |
| | 2.7449 | 269 | 0.0002 | |
| | 2.7551 | 270 | 0.0003 | |
| | 2.7653 | 271 | 0.0002 | |
| | 2.7755 | 272 | 0.0002 | |
| | 2.7857 | 273 | 0.0002 | |
| | 2.7959 | 274 | 0.0004 | |
| | 2.8061 | 275 | 0.0002 | |
| | 2.8163 | 276 | 0.0003 | |
| | 2.8265 | 277 | 0.0002 | |
| | 2.8367 | 278 | 0.0002 | |
| | 2.8469 | 279 | 0.0004 | |
| | 2.8571 | 280 | 0.0004 | |
| | 2.8673 | 281 | 0.0004 | |
| | 2.8776 | 282 | 0.0002 | |
| | 2.8878 | 283 | 0.0002 | |
| | 2.8980 | 284 | 0.0004 | |
| | 2.9082 | 285 | 0.0002 | |
| | 2.9184 | 286 | 0.0002 | |
| | 2.9286 | 287 | 0.0004 | |
| | 2.9388 | 288 | 0.0003 | |
| | 2.9490 | 289 | 0.0002 | |
| | 2.9592 | 290 | 0.0006 | |
| | 2.9694 | 291 | 0.0002 | |
| | 2.9796 | 292 | 0.0003 | |
| | 2.9898 | 293 | 0.0003 | |
| | 3.0 | 294 | 0.0002 | |
| | 3.0102 | 295 | 0.0002 | |
| | 3.0204 | 296 | 0.0001 | |
| | 3.0306 | 297 | 0.0002 | |
| | 3.0408 | 298 | 0.0005 | |
| | 3.0510 | 299 | 0.0004 | |
| | 3.0612 | 300 | 0.0005 | |
| | 3.0714 | 301 | 0.0002 | |
| | 3.0816 | 302 | 0.0002 | |
| | 3.0918 | 303 | 0.0002 | |
| | 3.1020 | 304 | 0.0004 | |
| | 3.1122 | 305 | 0.0002 | |
| | 3.1224 | 306 | 0.0002 | |
| | 3.1327 | 307 | 0.0002 | |
| | 3.1429 | 308 | 0.0002 | |
| | 3.1531 | 309 | 0.0003 | |
| | 3.1633 | 310 | 0.0003 | |
| | 3.1735 | 311 | 0.0002 | |
| | 3.1837 | 312 | 0.0004 | |
| | 3.1939 | 313 | 0.0002 | |
| | 3.2041 | 314 | 0.0001 | |
| | 3.2143 | 315 | 0.0002 | |
| | 3.2245 | 316 | 0.0004 | |
| | 3.2347 | 317 | 0.0004 | |
| | 3.2449 | 318 | 0.0003 | |
| | 3.2551 | 319 | 0.0002 | |
| | 3.2653 | 320 | 0.0002 | |
| | 3.2755 | 321 | 0.0002 | |
| | 3.2857 | 322 | 0.0003 | |
| | 3.2959 | 323 | 0.0003 | |
| | 3.3061 | 324 | 0.0003 | |
| | 3.3163 | 325 | 0.0002 | |
| | 3.3265 | 326 | 0.0002 | |
| | 3.3367 | 327 | 0.0001 | |
| | 3.3469 | 328 | 0.0002 | |
| | 3.3571 | 329 | 0.0004 | |
| | 3.3673 | 330 | 0.0002 | |
| | 3.3776 | 331 | 0.0002 | |
| | 3.3878 | 332 | 0.0002 | |
| | 3.3980 | 333 | 0.0001 | |
| | 3.4082 | 334 | 0.0002 | |
| | 3.4184 | 335 | 0.0002 | |
| | 3.4286 | 336 | 0.0001 | |
| | 3.4388 | 337 | 0.0005 | |
| | 3.4490 | 338 | 0.0001 | |
| | 3.4592 | 339 | 0.0003 | |
| | 3.4694 | 340 | 0.0003 | |
| | 3.4796 | 341 | 0.0002 | |
| | 3.4898 | 342 | 0.0002 | |
| | 3.5 | 343 | 0.0001 | |
| | 3.5102 | 344 | 0.0002 | |
| | 3.5204 | 345 | 0.0008 | |
| | 3.5306 | 346 | 0.0002 | |
| | 3.5408 | 347 | 0.0003 | |
| | 3.5510 | 348 | 0.0003 | |
| | 3.5612 | 349 | 0.0003 | |
| | 3.5714 | 350 | 0.0002 | |
| | 3.5816 | 351 | 0.0002 | |
| | 3.5918 | 352 | 0.0002 | |
| | 3.6020 | 353 | 0.0001 | |
| | 3.6122 | 354 | 0.0002 | |
| | 3.6224 | 355 | 0.0001 | |
| | 3.6327 | 356 | 0.0002 | |
| | 3.6429 | 357 | 0.0001 | |
| | 3.6531 | 358 | 0.0001 | |
| | 3.6633 | 359 | 0.0003 | |
| | 3.6735 | 360 | 0.0003 | |
| | 3.6837 | 361 | 0.0002 | |
| | 3.6939 | 362 | 0.0002 | |
| | 3.7041 | 363 | 0.0001 | |
| | 3.7143 | 364 | 0.0003 | |
| | 3.7245 | 365 | 0.0003 | |
| | 3.7347 | 366 | 0.0002 | |
| | 3.7449 | 367 | 0.0006 | |
| | 3.7551 | 368 | 0.0003 | |
| | 3.7653 | 369 | 0.0002 | |
| | 3.7755 | 370 | 0.0002 | |
| | 3.7857 | 371 | 0.0001 | |
| | 3.7959 | 372 | 0.0002 | |
| | 3.8061 | 373 | 0.0002 | |
| | 3.8163 | 374 | 0.0003 | |
| | 3.8265 | 375 | 0.0001 | |
| | 3.8367 | 376 | 0.0002 | |
| | 3.8469 | 377 | 0.0004 | |
| | 3.8571 | 378 | 0.0002 | |
| | 3.8673 | 379 | 0.0003 | |
| | 3.8776 | 380 | 0.0001 | |
| | 3.8878 | 381 | 0.0003 | |
| | 3.8980 | 382 | 0.0001 | |
| | 3.9082 | 383 | 0.0002 | |
| | 3.9184 | 384 | 0.0002 | |
| | 3.9286 | 385 | 0.0002 | |
| | 3.9388 | 386 | 0.0003 | |
| | 3.9490 | 387 | 0.0002 | |
| | 3.9592 | 388 | 0.0002 | |
| | 3.9694 | 389 | 0.0001 | |
| | 3.9796 | 390 | 0.0002 | |
| | 3.9898 | 391 | 0.0001 | |
| | 4.0 | 392 | 0.0001 | |
| | 4.0102 | 393 | 0.0001 | |
| | 4.0204 | 394 | 0.0002 | |
| | 4.0306 | 395 | 0.0001 | |
| | 4.0408 | 396 | 0.0007 | |
| | 4.0510 | 397 | 0.0002 | |
| | 4.0612 | 398 | 0.0002 | |
| | 4.0714 | 399 | 0.0001 | |
| | 4.0816 | 400 | 0.0001 | |
| | 4.0918 | 401 | 0.0002 | |
| | 4.1020 | 402 | 0.0002 | |
| | 4.1122 | 403 | 0.0001 | |
| | 4.1224 | 404 | 0.0001 | |
| | 4.1327 | 405 | 0.0002 | |
| | 4.1429 | 406 | 0.0004 | |
| | 4.1531 | 407 | 0.0004 | |
| | 4.1633 | 408 | 0.0006 | |
| | 4.1735 | 409 | 0.0001 | |
| | 4.1837 | 410 | 0.0002 | |
| | 4.1939 | 411 | 0.0002 | |
| | 4.2041 | 412 | 0.0001 | |
| | 4.2143 | 413 | 0.0001 | |
| | 4.2245 | 414 | 0.0001 | |
| | 4.2347 | 415 | 0.0001 | |
| | 4.2449 | 416 | 0.0003 | |
| | 4.2551 | 417 | 0.0001 | |
| | 4.2653 | 418 | 0.0002 | |
| | 4.2755 | 419 | 0.0001 | |
| | 4.2857 | 420 | 0.0002 | |
| | 4.2959 | 421 | 0.0003 | |
| | 4.3061 | 422 | 0.0004 | |
| | 4.3163 | 423 | 0.0002 | |
| | 4.3265 | 424 | 0.0003 | |
| | 4.3367 | 425 | 0.0001 | |
| | 4.3469 | 426 | 0.0001 | |
| | 4.3571 | 427 | 0.0002 | |
| | 4.3673 | 428 | 0.0002 | |
| | 4.3776 | 429 | 0.0002 | |
| | 4.3878 | 430 | 0.0002 | |
| | 4.3980 | 431 | 0.0002 | |
| | 4.4082 | 432 | 0.0001 | |
| | 4.4184 | 433 | 0.0003 | |
| | 4.4286 | 434 | 0.0002 | |
| | 4.4388 | 435 | 0.0003 | |
| | 4.4490 | 436 | 0.0003 | |
| | 4.4592 | 437 | 0.0003 | |
| | 4.4694 | 438 | 0.0001 | |
| | 4.4796 | 439 | 0.0002 | |
| | 4.4898 | 440 | 0.0002 | |
| | 4.5 | 441 | 0.0002 | |
| | 4.5102 | 442 | 0.0003 | |
| | 4.5204 | 443 | 0.0003 | |
| | 4.5306 | 444 | 0.0002 | |
| | 4.5408 | 445 | 0.0002 | |
| | 4.5510 | 446 | 0.0001 | |
| | 4.5612 | 447 | 0.0002 | |
| | 4.5714 | 448 | 0.0002 | |
| | 4.5816 | 449 | 0.0001 | |
| | 4.5918 | 450 | 0.0002 | |
| | 4.6020 | 451 | 0.0002 | |
| | 4.6122 | 452 | 0.0001 | |
| | 4.6224 | 453 | 0.0003 | |
| | 4.6327 | 454 | 0.0002 | |
| | 4.6429 | 455 | 0.0001 | |
| | 4.6531 | 456 | 0.0001 | |
| | 4.6633 | 457 | 0.0001 | |
| | 4.6735 | 458 | 0.0001 | |
| | 4.6837 | 459 | 0.0002 | |
| | 4.6939 | 460 | 0.0001 | |
| | 4.7041 | 461 | 0.0002 | |
| | 4.7143 | 462 | 0.0001 | |
| | 4.7245 | 463 | 0.0001 | |
| | 4.7347 | 464 | 0.0002 | |
| | 4.7449 | 465 | 0.0002 | |
| | 4.7551 | 466 | 0.0001 | |
| | 4.7653 | 467 | 0.0002 | |
| | 4.7755 | 468 | 0.0002 | |
| | 4.7857 | 469 | 0.0002 | |
| | 4.7959 | 470 | 0.0002 | |
| | 4.8061 | 471 | 0.0007 | |
| | 4.8163 | 472 | 0.0002 | |
| | 4.8265 | 473 | 0.0006 | |
| | 4.8367 | 474 | 0.0002 | |
| | 4.8469 | 475 | 0.0001 | |
| | 4.8571 | 476 | 0.0002 | |
| | 4.8673 | 477 | 0.0001 | |
| | 4.8776 | 478 | 0.0002 | |
| | 4.8878 | 479 | 0.0002 | |
| | 4.8980 | 480 | 0.0003 | |
| | 4.9082 | 481 | 0.0002 | |
| | 4.9184 | 482 | 0.0001 | |
| | 4.9286 | 483 | 0.0002 | |
| | 4.9388 | 484 | 0.0002 | |
| | 4.9490 | 485 | 0.0002 | |
| | 4.9592 | 486 | 0.0002 | |
| | 4.9694 | 487 | 0.0002 | |
| | 4.9796 | 488 | 0.0002 | |
| | 4.9898 | 489 | 0.0004 | |
| | 5.0 | 490 | 0.0002 | |
|
|
| </details> |
|
|
| ### Framework Versions |
| - Python: 3.11.13 |
| - Sentence Transformers: 4.1.0 |
| - Transformers: 4.52.3 |
| - PyTorch: 2.6.0+cu124 |
| - Accelerate: 1.7.0 |
| - Datasets: 2.14.4 |
| - Tokenizers: 0.21.1 |
|
|
| ## 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", |
| } |
| ``` |
|
|
| #### ContrastiveLoss |
| ```bibtex |
| @inproceedings{hadsell2006dimensionality, |
| author={Hadsell, R. and Chopra, S. and LeCun, Y.}, |
| booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, |
| title={Dimensionality Reduction by Learning an Invariant Mapping}, |
| year={2006}, |
| volume={2}, |
| number={}, |
| pages={1735-1742}, |
| doi={10.1109/CVPR.2006.100} |
| } |
| ``` |
|
|
| <!-- |
| ## Glossary |
|
|
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
|
|
| <!-- |
| ## Model Card Authors |
|
|
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
|
|
| <!-- |
| ## Model Card Contact |
|
|
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
| --> |
