| | --- |
| | language: |
| | - en |
| | license: apache-2.0 |
| | tags: |
| | - sentence-transformers |
| | - sentence-similarity |
| | - feature-extraction |
| | - dense |
| | - generated_from_trainer |
| | - dataset_size:91097 |
| | - loss:MultipleNegativesRankingLoss |
| | base_model: NeuML/pubmedbert-base-embeddings |
| | widget: |
| | - source_sentence: '<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>provider gen suffix<VALUE>Jr, |
| | Sr, II, III||III||III||M||II||Jr ||III||Jr.||Sr. ' |
| | sentences: |
| | - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Gen suffix |
| | - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Attr-OBSERVATION |
| | - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>State License |
| | - source_sentence: <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Trust-Based Relational Intervention |
| | (TBRI)<VALUE>FALSE |
| | sentences: |
| | - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Attr-CHILD PSYCHOLOGY |
| | - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Supervising Physician NPI |
| | - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Attr-Trust-Based Relational Intervention (TBRI) |
| | - source_sentence: '<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>action<VALUE>Now has AMS Medicare_x000D_ |
| | |
| | Effective Date 10/7/2023_x000D_ |
| | |
| | TIN 74-1613878||Effective 11/7/2023 - Previously credentialed; please add to Baylor |
| | contract and fee schedules under TIN 74-1613878 and link to all applicable Baylor |
| | products/networks. Close Panel and Directory Suppress as they are Hospital Based.||Now |
| | Enrolled in AMS Medicaid_x000D_ |
| | |
| | Effective Date 8/1/2023 _x000D_ |
| | |
| | TIN 74-1613878' |
| | sentences: |
| | - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Attr-OBSERVATION |
| | - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Attr-Autism/Applied Behavioral Analysis (ABA) |
| | - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Change Request Type |
| | - source_sentence: <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>language_written_indicator<VALUE>YES||NO |
| | sentences: |
| | - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Medicare Number |
| | - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Language Written? |
| | - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Education Specialty |
| | - source_sentence: <DOMAIN>ROSTER<TASK>COL_MAP<TEXT> affiliate address<VALUE>200 E |
| | ARIZONA||3480 E GUASTI RD |
| | sentences: |
| | - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Hospital State |
| | - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Hospital Addr Line 1 |
| | - <DOMAIN>ROSTER<TASK>COL_MAP<TEXT>State License |
| | pipeline_tag: sentence-similarity |
| | library_name: sentence-transformers |
| | metrics: |
| | - cosine_accuracy |
| | model-index: |
| | - name: txt_std_ra_automapper_molina |
| | results: |
| | - task: |
| | type: triplet |
| | name: Triplet |
| | dataset: |
| | name: txt std ra automapper molina |
| | type: txt_std_ra_automapper_molina |
| | metrics: |
| | - type: cosine_accuracy |
| | value: 0.9426305294036865 |
| | name: Cosine Accuracy |
| | --- |
| | |
| | # txt_std_ra_automapper_molina |
| |
|
| | This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [NeuML/pubmedbert-base-embeddings](https://huggingface.co/NeuML/pubmedbert-base-embeddings). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
| |
|
| | ## Model Details |
| |
|
| | ### Model Description |
| | - **Model Type:** Sentence Transformer |
| | - **Base model:** [NeuML/pubmedbert-base-embeddings](https://huggingface.co/NeuML/pubmedbert-base-embeddings) <!-- at revision d6eaca8254bc229f3ca42749a5510ae287eb3486 --> |
| | - **Maximum Sequence Length:** 512 tokens |
| | - **Output Dimensionality:** 768 dimensions |
| | - **Similarity Function:** Cosine Similarity |
| | <!-- - **Training Dataset:** Unknown --> |
| | - **Language:** en |
| | - **License:** apache-2.0 |
| |
|
| | ### 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': 512, 'do_lower_case': False, 'architecture': 'BertModel'}) |
| | (1): Pooling({'word_embedding_dimension': 768, '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}) |
| | ) |
| | ``` |
| |
|
| | ## 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 = [ |
| | '<DOMAIN>ROSTER<TASK>COL_MAP<TEXT> affiliate address<VALUE>200 E ARIZONA||3480 E GUASTI RD', |
| | '<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Hospital Addr Line 1', |
| | '<DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Hospital State', |
| | ] |
| | embeddings = model.encode(sentences) |
| | print(embeddings.shape) |
| | # [3, 768] |
| | |
| | # Get the similarity scores for the embeddings |
| | similarities = model.similarity(embeddings, embeddings) |
| | print(similarities) |
| | # tensor([[1.0000, 0.9630, 0.0580], |
| | # [0.9630, 1.0000, 0.0549], |
| | # [0.0580, 0.0549, 1.0000]]) |
| | ``` |
| |
|
| | <!-- |
| | ### 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 |
| |
|
| | #### Triplet |
| |
|
| | * Dataset: `txt_std_ra_automapper_molina` |
| | * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
| |
|
| | | Metric | Value | |
| | |:--------------------|:-----------| |
| | | **cosine_accuracy** | **0.9426** | |
| | |
| | <!-- |
| | ## 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: 91,097 training samples |
| | * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
| | * Approximate statistics based on the first 1000 samples: |
| | | | anchor | positive | negative | |
| | |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
| | | type | string | string | string | |
| | | details | <ul><li>min: 21 tokens</li><li>mean: 45.98 tokens</li><li>max: 185 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 19.65 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 19.86 tokens</li><li>max: 30 tokens</li></ul> | |
| | * Samples: |
| | | anchor | positive | negative | |
| | |:--------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------|:---------------------------------------------------------------------------------------| |
| | | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>license from (01/01/1901)<VALUE>03-11-2021</code> | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>License effective date</code> | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Provider Term Reason</code> | |
| | | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>taxid<VALUE>994566602\|\|954558726\|\|431857688\|\|06-0873021\|\|611313932\|\|202530877</code> | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>TIN</code> | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Min Age</code> | |
| | | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>pri tax id number<VALUE>920379978</code> | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>TIN</code> | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Attr-Intellectual Disability Development</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 |
| | } |
| | ``` |
| | |
| | ### Evaluation Dataset |
| | |
| | #### Unnamed Dataset |
| | |
| | * Size: 9,709 evaluation samples |
| | * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
| | * Approximate statistics based on the first 1000 samples: |
| | | | anchor | positive | negative | |
| | |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
| | | type | string | string | string | |
| | | details | <ul><li>min: 21 tokens</li><li>mean: 46.15 tokens</li><li>max: 253 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 19.7 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 19.94 tokens</li><li>max: 30 tokens</li></ul> | |
| | * Samples: |
| | | anchor | positive | negative | |
| | |:------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------| |
| | | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>provider term date (01/01/1901)<VALUE>11-02-2027</code> | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Provider term Date</code> | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Network Directory Suppression Reason Code</code> | |
| | | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Supervising Physician Address Line 2<VALUE>Ste 300Tvw\|\|SUITE 1A\|\|#206\|\|490 Route 304\|\|Suite 210</code> | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Supervising Physician Address Line 2</code> | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Hospital Term Date</code> | |
| | | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>education1_specialty<VALUE>Infectious Disease\|\|Pediatrics</code> | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Education Specialty</code> | <code><DOMAIN>ROSTER<TASK>COL_MAP<TEXT>Supervising Physician NPI</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 |
| | } |
| | ``` |
| | |
| | ### Training Hyperparameters |
| | #### Non-Default Hyperparameters |
| | |
| | - `eval_strategy`: steps |
| | - `per_device_train_batch_size`: 16 |
| | - `per_device_eval_batch_size`: 16 |
| | - `num_train_epochs`: 2 |
| | - `warmup_ratio`: 0.1 |
| | - `fp16`: True |
| | - `load_best_model_at_end`: True |
| | - `batch_sampler`: no_duplicates |
| | |
| | #### All Hyperparameters |
| | <details><summary>Click to expand</summary> |
| | |
| | - `overwrite_output_dir`: False |
| | - `do_predict`: False |
| | - `eval_strategy`: steps |
| | - `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`: 5e-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`: False |
| | - `fp16`: True |
| | - `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`: True |
| | - `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_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`: no_duplicates |
| | - `multi_dataset_batch_sampler`: proportional |
| | - `router_mapping`: {} |
| | - `learning_rate_mapping`: {} |
| | |
| | </details> |
| | |
| | ### Training Logs |
| | <details><summary>Click to expand</summary> |
| | |
| | | Epoch | Step | Training Loss | Validation Loss | txt_std_ra_automapper_molina_cosine_accuracy | |
| | |:---------:|:--------:|:-------------:|:---------------:|:--------------------------------------------:| |
| | | -1 | -1 | - | - | 0.9426 | |
| | | 0.0088 | 50 | 1.7127 | - | - | |
| | | 0.0176 | 100 | 0.8476 | - | - | |
| | | 0.0263 | 150 | 0.5572 | - | - | |
| | | 0.0351 | 200 | 0.4306 | 0.2925 | - | |
| | | 0.0439 | 250 | 0.3464 | - | - | |
| | | 0.0527 | 300 | 0.2764 | - | - | |
| | | 0.0615 | 350 | 0.2381 | - | - | |
| | | 0.0702 | 400 | 0.1622 | 0.1312 | - | |
| | | 0.0790 | 450 | 0.1617 | - | - | |
| | | 0.0878 | 500 | 0.1492 | - | - | |
| | | 0.0966 | 550 | 0.1429 | - | - | |
| | | 0.1054 | 600 | 0.114 | 0.0835 | - | |
| | | 0.1142 | 650 | 0.1203 | - | - | |
| | | 0.1229 | 700 | 0.0901 | - | - | |
| | | 0.1317 | 750 | 0.1014 | - | - | |
| | | 0.1405 | 800 | 0.0796 | 0.0614 | - | |
| | | 0.1493 | 850 | 0.0631 | - | - | |
| | | 0.1581 | 900 | 0.0989 | - | - | |
| | | 0.1668 | 950 | 0.0627 | - | - | |
| | | 0.1756 | 1000 | 0.0809 | 0.0670 | - | |
| | | 0.1844 | 1050 | 0.0638 | - | - | |
| | | 0.1932 | 1100 | 0.0664 | - | - | |
| | | 0.2020 | 1150 | 0.0419 | - | - | |
| | | 0.2107 | 1200 | 0.0569 | 0.0506 | - | |
| | | 0.2195 | 1250 | 0.0842 | - | - | |
| | | 0.2283 | 1300 | 0.0557 | - | - | |
| | | 0.2371 | 1350 | 0.0653 | - | - | |
| | | 0.2459 | 1400 | 0.065 | 0.0438 | - | |
| | | 0.2547 | 1450 | 0.0459 | - | - | |
| | | 0.2634 | 1500 | 0.0644 | - | - | |
| | | 0.2722 | 1550 | 0.0494 | - | - | |
| | | 0.2810 | 1600 | 0.0532 | 0.0296 | - | |
| | | 0.2898 | 1650 | 0.0792 | - | - | |
| | | 0.2986 | 1700 | 0.0592 | - | - | |
| | | 0.3073 | 1750 | 0.0503 | - | - | |
| | | 0.3161 | 1800 | 0.0353 | 0.0375 | - | |
| | | 0.3249 | 1850 | 0.0556 | - | - | |
| | | 0.3337 | 1900 | 0.0545 | - | - | |
| | | 0.3425 | 1950 | 0.035 | - | - | |
| | | 0.3512 | 2000 | 0.0286 | 0.0219 | - | |
| | | 0.3600 | 2050 | 0.0305 | - | - | |
| | | 0.3688 | 2100 | 0.014 | - | - | |
| | | 0.3776 | 2150 | 0.0307 | - | - | |
| | | 0.3864 | 2200 | 0.0374 | 0.0242 | - | |
| | | 0.3952 | 2250 | 0.039 | - | - | |
| | | 0.4039 | 2300 | 0.0218 | - | - | |
| | | 0.4127 | 2350 | 0.0416 | - | - | |
| | | 0.4215 | 2400 | 0.038 | 0.0186 | - | |
| | | 0.4303 | 2450 | 0.0282 | - | - | |
| | | 0.4391 | 2500 | 0.0171 | - | - | |
| | | 0.4478 | 2550 | 0.0282 | - | - | |
| | | 0.4566 | 2600 | 0.0227 | 0.0185 | - | |
| | | 0.4654 | 2650 | 0.02 | - | - | |
| | | 0.4742 | 2700 | 0.0215 | - | - | |
| | | 0.4830 | 2750 | 0.0328 | - | - | |
| | | 0.4917 | 2800 | 0.0118 | 0.0165 | - | |
| | | 0.5005 | 2850 | 0.0278 | - | - | |
| | | 0.5093 | 2900 | 0.0072 | - | - | |
| | | 0.5181 | 2950 | 0.0252 | - | - | |
| | | 0.5269 | 3000 | 0.0162 | 0.0151 | - | |
| | | 0.5357 | 3050 | 0.0241 | - | - | |
| | | 0.5444 | 3100 | 0.0042 | - | - | |
| | | 0.5532 | 3150 | 0.0157 | - | - | |
| | | 0.5620 | 3200 | 0.0256 | 0.0141 | - | |
| | | 0.5708 | 3250 | 0.0106 | - | - | |
| | | 0.5796 | 3300 | 0.0138 | - | - | |
| | | 0.5883 | 3350 | 0.0292 | - | - | |
| | | 0.5971 | 3400 | 0.0133 | 0.0164 | - | |
| | | 0.6059 | 3450 | 0.0105 | - | - | |
| | | 0.6147 | 3500 | 0.0148 | - | - | |
| | | 0.6235 | 3550 | 0.0101 | - | - | |
| | | 0.6322 | 3600 | 0.0101 | 0.0136 | - | |
| | | 0.6410 | 3650 | 0.0271 | - | - | |
| | | 0.6498 | 3700 | 0.028 | - | - | |
| | | 0.6586 | 3750 | 0.0057 | - | - | |
| | | 0.6674 | 3800 | 0.0273 | 0.0101 | - | |
| | | 0.6762 | 3850 | 0.0201 | - | - | |
| | | 0.6849 | 3900 | 0.0164 | - | - | |
| | | 0.6937 | 3950 | 0.0425 | - | - | |
| | | 0.7025 | 4000 | 0.0168 | 0.0112 | - | |
| | | 0.7113 | 4050 | 0.0174 | - | - | |
| | | 0.7201 | 4100 | 0.0153 | - | - | |
| | | 0.7288 | 4150 | 0.0166 | - | - | |
| | | 0.7376 | 4200 | 0.0252 | 0.0078 | - | |
| | | 0.7464 | 4250 | 0.0098 | - | - | |
| | | 0.7552 | 4300 | 0.0145 | - | - | |
| | | 0.7640 | 4350 | 0.0141 | - | - | |
| | | 0.7727 | 4400 | 0.0119 | 0.0088 | - | |
| | | 0.7815 | 4450 | 0.0108 | - | - | |
| | | 0.7903 | 4500 | 0.0146 | - | - | |
| | | 0.7991 | 4550 | 0.0104 | - | - | |
| | | 0.8079 | 4600 | 0.0068 | 0.0116 | - | |
| | | 0.8166 | 4650 | 0.0233 | - | - | |
| | | 0.8254 | 4700 | 0.0028 | - | - | |
| | | 0.8342 | 4750 | 0.0255 | - | - | |
| | | 0.8430 | 4800 | 0.009 | 0.0127 | - | |
| | | 0.8518 | 4850 | 0.0293 | - | - | |
| | | 0.8606 | 4900 | 0.0045 | - | - | |
| | | 0.8693 | 4950 | 0.0048 | - | - | |
| | | 0.8781 | 5000 | 0.0178 | 0.0132 | - | |
| | | 0.8869 | 5050 | 0.0059 | - | - | |
| | | 0.8957 | 5100 | 0.0221 | - | - | |
| | | 0.9045 | 5150 | 0.0082 | - | - | |
| | | 0.9132 | 5200 | 0.0111 | 0.0097 | - | |
| | | 0.9220 | 5250 | 0.0021 | - | - | |
| | | 0.9308 | 5300 | 0.0034 | - | - | |
| | | 0.9396 | 5350 | 0.0449 | - | - | |
| | | 0.9484 | 5400 | 0.0128 | 0.0066 | - | |
| | | 0.9571 | 5450 | 0.0095 | - | - | |
| | | 0.9659 | 5500 | 0.009 | - | - | |
| | | 0.9747 | 5550 | 0.0169 | - | - | |
| | | 0.9835 | 5600 | 0.0115 | 0.0060 | - | |
| | | 0.9923 | 5650 | 0.0204 | - | - | |
| | | 1.0011 | 5700 | 0.0116 | - | - | |
| | | 1.0098 | 5750 | 0.0049 | - | - | |
| | | 1.0186 | 5800 | 0.0064 | 0.0096 | - | |
| | | 1.0274 | 5850 | 0.0061 | - | - | |
| | | 1.0362 | 5900 | 0.0011 | - | - | |
| | | 1.0450 | 5950 | 0.018 | - | - | |
| | | 1.0537 | 6000 | 0.0231 | 0.0056 | - | |
| | | 1.0625 | 6050 | 0.0081 | - | - | |
| | | 1.0713 | 6100 | 0.0021 | - | - | |
| | | 1.0801 | 6150 | 0.006 | - | - | |
| | | 1.0889 | 6200 | 0.0116 | 0.0078 | - | |
| | | 1.0976 | 6250 | 0.0074 | - | - | |
| | | 1.1064 | 6300 | 0.0082 | - | - | |
| | | 1.1152 | 6350 | 0.0011 | - | - | |
| | | 1.1240 | 6400 | 0.0051 | 0.0101 | - | |
| | | 1.1328 | 6450 | 0.007 | - | - | |
| | | 1.1416 | 6500 | 0.0015 | - | - | |
| | | 1.1503 | 6550 | 0.0037 | - | - | |
| | | 1.1591 | 6600 | 0.0027 | 0.0073 | - | |
| | | 1.1679 | 6650 | 0.0005 | - | - | |
| | | 1.1767 | 6700 | 0.0239 | - | - | |
| | | 1.1855 | 6750 | 0.0136 | - | - | |
| | | 1.1942 | 6800 | 0.0251 | 0.0070 | - | |
| | | 1.2030 | 6850 | 0.0004 | - | - | |
| | | 1.2118 | 6900 | 0.0065 | - | - | |
| | | 1.2206 | 6950 | 0.0109 | - | - | |
| | | 1.2294 | 7000 | 0.0009 | 0.0043 | - | |
| | | 1.2381 | 7050 | 0.0086 | - | - | |
| | | 1.2469 | 7100 | 0.003 | - | - | |
| | | 1.2557 | 7150 | 0.0044 | - | - | |
| | | 1.2645 | 7200 | 0.0118 | 0.0058 | - | |
| | | 1.2733 | 7250 | 0.0093 | - | - | |
| | | 1.2821 | 7300 | 0.0023 | - | - | |
| | | 1.2908 | 7350 | 0.002 | - | - | |
| | | 1.2996 | 7400 | 0.007 | 0.0061 | - | |
| | | 1.3084 | 7450 | 0.0162 | - | - | |
| | | 1.3172 | 7500 | 0.0011 | - | - | |
| | | 1.3260 | 7550 | 0.007 | - | - | |
| | | 1.3347 | 7600 | 0.0014 | 0.0048 | - | |
| | | 1.3435 | 7650 | 0.0033 | - | - | |
| | | 1.3523 | 7700 | 0.0007 | - | - | |
| | | 1.3611 | 7750 | 0.0017 | - | - | |
| | | 1.3699 | 7800 | 0.0078 | 0.0049 | - | |
| | | 1.3786 | 7850 | 0.0049 | - | - | |
| | | 1.3874 | 7900 | 0.003 | - | - | |
| | | 1.3962 | 7950 | 0.0028 | - | - | |
| | | 1.4050 | 8000 | 0.0038 | 0.0033 | - | |
| | | 1.4138 | 8050 | 0.0158 | - | - | |
| | | 1.4226 | 8100 | 0.0008 | - | - | |
| | | 1.4313 | 8150 | 0.0007 | - | - | |
| | | 1.4401 | 8200 | 0.0038 | 0.0024 | - | |
| | | 1.4489 | 8250 | 0.0177 | - | - | |
| | | 1.4577 | 8300 | 0.0044 | - | - | |
| | | 1.4665 | 8350 | 0.0064 | - | - | |
| | | 1.4752 | 8400 | 0.0005 | 0.0049 | - | |
| | | 1.4840 | 8450 | 0.0146 | - | - | |
| | | 1.4928 | 8500 | 0.001 | - | - | |
| | | 1.5016 | 8550 | 0.0014 | - | - | |
| | | 1.5104 | 8600 | 0.0041 | 0.0038 | - | |
| | | 1.5191 | 8650 | 0.0072 | - | - | |
| | | 1.5279 | 8700 | 0.0014 | - | - | |
| | | 1.5367 | 8750 | 0.0135 | - | - | |
| | | 1.5455 | 8800 | 0.0148 | 0.0039 | - | |
| | | 1.5543 | 8850 | 0.0017 | - | - | |
| | | 1.5630 | 8900 | 0.007 | - | - | |
| | | 1.5718 | 8950 | 0.012 | - | - | |
| | | 1.5806 | 9000 | 0.0004 | 0.0024 | - | |
| | | 1.5894 | 9050 | 0.0026 | - | - | |
| | | 1.5982 | 9100 | 0.0109 | - | - | |
| | | 1.6070 | 9150 | 0.0009 | - | - | |
| | | 1.6157 | 9200 | 0.0054 | 0.0022 | - | |
| | | 1.6245 | 9250 | 0.0032 | - | - | |
| | | 1.6333 | 9300 | 0.0135 | - | - | |
| | | 1.6421 | 9350 | 0.0131 | - | - | |
| | | 1.6509 | 9400 | 0.0049 | 0.0021 | - | |
| | | 1.6596 | 9450 | 0.0003 | - | - | |
| | | 1.6684 | 9500 | 0.0027 | - | - | |
| | | 1.6772 | 9550 | 0.0008 | - | - | |
| | | **1.686** | **9600** | **0.0124** | **0.002** | **-** | |
| | | 1.6948 | 9650 | 0.0026 | - | - | |
| | | 1.7035 | 9700 | 0.004 | - | - | |
| | | 1.7123 | 9750 | 0.0008 | - | - | |
| | | 1.7211 | 9800 | 0.0058 | 0.0028 | - | |
| | | 1.7299 | 9850 | 0.0133 | - | - | |
| | | 1.7387 | 9900 | 0.0005 | - | - | |
| | | 1.7475 | 9950 | 0.0007 | - | - | |
| | | 1.7562 | 10000 | 0.0009 | 0.0040 | - | |
| | | 1.7650 | 10050 | 0.0018 | - | - | |
| | | 1.7738 | 10100 | 0.0002 | - | - | |
| | | 1.7826 | 10150 | 0.002 | - | - | |
| | | 1.7914 | 10200 | 0.0021 | 0.0042 | - | |
| | | 1.8001 | 10250 | 0.002 | - | - | |
| | | 1.8089 | 10300 | 0.0003 | - | - | |
| | | 1.8177 | 10350 | 0.001 | - | - | |
| | | 1.8265 | 10400 | 0.0092 | 0.0045 | - | |
| | | 1.8353 | 10450 | 0.0032 | - | - | |
| | | 1.8440 | 10500 | 0.0002 | - | - | |
| | | 1.8528 | 10550 | 0.0026 | - | - | |
| | | 1.8616 | 10600 | 0.0003 | 0.0048 | - | |
| | | 1.8704 | 10650 | 0.001 | - | - | |
| | | 1.8792 | 10700 | 0.0126 | - | - | |
| | | 1.8880 | 10750 | 0.0172 | - | - | |
| | | 1.8967 | 10800 | 0.0002 | 0.0034 | - | |
| | | 1.9055 | 10850 | 0.0038 | - | - | |
| | | 1.9143 | 10900 | 0.0005 | - | - | |
| | | 1.9231 | 10950 | 0.0001 | - | - | |
| | | 1.9319 | 11000 | 0.0162 | 0.0033 | - | |
| | | 1.9406 | 11050 | 0.0037 | - | - | |
| | | 1.9494 | 11100 | 0.0003 | - | - | |
| | | 1.9582 | 11150 | 0.0021 | - | - | |
| | | 1.9670 | 11200 | 0.0098 | 0.0032 | - | |
| | | 1.9758 | 11250 | 0.0003 | - | - | |
| | | 1.9845 | 11300 | 0.0017 | - | - | |
| | | 1.9933 | 11350 | 0.0048 | - | - | |
| | |
| | * The bold row denotes the saved checkpoint. |
| | </details> |
| | |
| | ### Framework Versions |
| | - Python: 3.10.17 |
| | - Sentence Transformers: 5.1.0 |
| | - Transformers: 4.55.0 |
| | - PyTorch: 2.8.0+cu128 |
| | - Accelerate: 1.7.0 |
| | - Datasets: 3.5.0 |
| | - Tokenizers: 0.21.4 |
| | |
| | ## 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{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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