Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "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
+}

README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:329355
+- loss:MultipleNegativesRankingLoss
+base_model: NeuML/pubmedbert-base-embeddings
+widget:
+- source_sentence: 'CONTEXT: check your other micronutrients B12, folate, your vitamin
+    D malabsorbed after surgery
+
+    REASONING: Folate listed among micronutrients to check.'
+  sentences:
+  - XR FOOT LT 3 View* (HUD)
+  - Folate, Serum (Request)* (Orchard)
+  - Wound Cleansing/Irrigation, Clinic
+- source_sentence: 'COMMAND: Submit a 99213 established patient office visit charge
+    for today’s evaluation and counseling.'
+  sentences:
+  - '[''99213 office o/p est low 20-29 min (Charge)'''
+  - 99213 office o/p est low 20-29 min (Charge)
+  - clotrimazole 1% topical cream
+- source_sentence: 'CONTEXT: pediatric patient with fever, sore throat, headache exam
+    of ears, throat, lungs; counseling on strep care school note discussed; pharmacy
+    confirmed; results to be called'
+  sentences:
+  - meloxicam 7.5 mg oral tablet
+  - 99213 office o/p est low 20-29 min (Charge)
+  - 99214 office o/p est mod 30-39 min (Charge)
+- source_sentence: 'CONTEXT: established patient follow-up style visit anxiety, tremor
+    assessment and medication counseling 30–40 minute detailed discussion management
+    options and side effects reviewed
+
+    REASONING: Extended medical decision-making on anxiety and essential tremor with
+    medication options and counseling consistent with a moderate complexity, mid-length
+    established patient visit.'
+  sentences:
+  - 99214 office o/p est mod 30-39 min (Charge)
+  - '''A4550 surgical trays (Charge)'''
+  - '[''99214 office o/p est mod 30-39 min (Charge)'']'
+- source_sentence: 'COMMAND: Have lab draw blood today per ordered tests.
+
+    CONTEXT: get a little blood work today they''re gonna get you to x-ray and lab
+    before you leave'
+  sentences:
+  - Thin Prep Pap w/ High Risk HPV, Over 30 Years old (Request)* (Orchard)
+  - Rocephin*
+  - 36415 blood draw, venipuncture (Charge)
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on NeuML/pubmedbert-base-embeddings
+
+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:** 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': 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("praphul555/jeda-stage-1")
+# Run inference
+sentences = [
+    "COMMAND: Have lab draw blood today per ordered tests.\nCONTEXT: get a little blood work today they're gonna get you to x-ray and lab before you leave",
+    '36415 blood draw, venipuncture (Charge)',
+    'Rocephin*',
+]
+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,  0.8369, -0.0363],
+#         [ 0.8369,  1.0000, -0.0708],
+#         [-0.0363, -0.0708,  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.*
+-->
+
+<!--
+## 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: 329,355 training samples
+* Columns: <code>text1</code> and <code>text2</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | text1                                                                             | text2                                                                             |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 8 tokens</li><li>mean: 37.3 tokens</li><li>max: 106 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 12.95 tokens</li><li>max: 24 tokens</li></ul> |
+* Samples:
+  | text1                                                                                                                                                                                                                                                                    | text2                                             |
+  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------|
+  | <code>COMMAND: Please arrange transport to radiology now and let them know we're sending him for a right foot/toe x-ray with weight-bearing views.<br>CONTEXT: wheel him over to x-ray x-ray right foot complete with weight-bearing views go tell the x-ray lady</code> | <code>SDC Radiology Transfer Communication</code> |
+  | <code>COMMAND: Please arrange transport to radiology now and let them know we're sending him for a right foot/toe x-ray with weight-bearing views.</code>                                                                                                                | <code>SDC Radiology Transfer Communication</code> |
+  | <code>CONTEXT: wheel him over to x-ray x-ray right foot complete with weight-bearing views go tell the x-ray lady<br>REASONING: Doctor instructs staff to transport patient to x-ray and communicate exam details.</code>                                                | <code>SDC Radiology Transfer Communication</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"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 64
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 5
+- `warmup_ratio`: 0.1
+- `seed`: 13
+- `batch_sampler`: no_duplicates
+
+#### 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`: 64
+- `per_device_eval_batch_size`: 8
+- `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`: 13
+- `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}
+- `parallelism_config`: 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
+- `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 |
+|:------:|:-----:|:-------------:|
+| 0.0097 | 50    | 2.3103        |
+| 0.0194 | 100   | 1.9798        |
+| 0.0291 | 150   | 1.6487        |
+| 0.0389 | 200   | 1.3829        |
+| 0.0486 | 250   | 1.25          |
+| 0.0583 | 300   | 1.1482        |
+| 0.0680 | 350   | 1.0997        |
+| 0.0777 | 400   | 1.0484        |
+| 0.0874 | 450   | 0.9522        |
+| 0.0971 | 500   | 0.9385        |
+| 0.1069 | 550   | 0.8914        |
+| 0.1166 | 600   | 0.86          |
+| 0.1263 | 650   | 0.8825        |
+| 0.1360 | 700   | 0.8217        |
+| 0.1457 | 750   | 0.8102        |
+| 0.1554 | 800   | 0.7831        |
+| 0.1651 | 850   | 0.796         |
+| 0.1749 | 900   | 0.7542        |
+| 0.1846 | 950   | 0.775         |
+| 0.1943 | 1000  | 0.7437        |
+| 0.2040 | 1050  | 0.7237        |
+| 0.2137 | 1100  | 0.6945        |
+| 0.2234 | 1150  | 0.6979        |
+| 0.2331 | 1200  | 0.6834        |
+| 0.2429 | 1250  | 0.7149        |
+| 0.2526 | 1300  | 0.6582        |
+| 0.2623 | 1350  | 0.6437        |
+| 0.2720 | 1400  | 0.6213        |
+| 0.2817 | 1450  | 0.6087        |
+| 0.2914 | 1500  | 0.6225        |
+| 0.3011 | 1550  | 0.5579        |
+| 0.3109 | 1600  | 0.6206        |
+| 0.3206 | 1650  | 0.5787        |
+| 0.3303 | 1700  | 0.5721        |
+| 0.3400 | 1750  | 0.5695        |
+| 0.3497 | 1800  | 0.5395        |
+| 0.3594 | 1850  | 0.5476        |
+| 0.3691 | 1900  | 0.5556        |
+| 0.3789 | 1950  | 0.5628        |
+| 0.3886 | 2000  | 0.5241        |
+| 0.3983 | 2050  | 0.5457        |
+| 0.4080 | 2100  | 0.5339        |
+| 0.4177 | 2150  | 0.5429        |
+| 0.4274 | 2200  | 0.5421        |
+| 0.4371 | 2250  | 0.5149        |
+| 0.4469 | 2300  | 0.5015        |
+| 0.4566 | 2350  | 0.5005        |
+| 0.4663 | 2400  | 0.5149        |
+| 0.4760 | 2450  | 0.5004        |
+| 0.4857 | 2500  | 0.4852        |
+| 0.4954 | 2550  | 0.5316        |
+| 0.5051 | 2600  | 0.5227        |
+| 0.5149 | 2650  | 0.5138        |
+| 0.5246 | 2700  | 0.4744        |
+| 0.5343 | 2750  | 0.4885        |
+| 0.5440 | 2800  | 0.5036        |
+| 0.5537 | 2850  | 0.5077        |
+| 0.5634 | 2900  | 0.4669        |
+| 0.5731 | 2950  | 0.4682        |
+| 0.5829 | 3000  | 0.4588        |
+| 0.5926 | 3050  | 0.4567        |
+| 0.6023 | 3100  | 0.4671        |
+| 0.6120 | 3150  | 0.5114        |
+| 0.6217 | 3200  | 0.4715        |
+| 0.6314 | 3250  | 0.4353        |
+| 0.6412 | 3300  | 0.46          |
+| 0.6509 | 3350  | 0.4525        |
+| 0.6606 | 3400  | 0.4633        |
+| 0.6703 | 3450  | 0.4344        |
+| 0.6800 | 3500  | 0.4566        |
+| 0.6897 | 3550  | 0.4643        |
+| 0.6994 | 3600  | 0.4615        |
+| 0.7092 | 3650  | 0.4387        |
+| 0.7189 | 3700  | 0.4145        |
+| 0.7286 | 3750  | 0.4646        |
+| 0.7383 | 3800  | 0.4831        |
+| 0.7480 | 3850  | 0.444         |
+| 0.7577 | 3900  | 0.4412        |
+| 0.7674 | 3950  | 0.4407        |
+| 0.7772 | 4000  | 0.4383        |
+| 0.7869 | 4050  | 0.4403        |
+| 0.7966 | 4100  | 0.4674        |
+| 0.8063 | 4150  | 0.4477        |
+| 0.8160 | 4200  | 0.4619        |
+| 0.8257 | 4250  | 0.4368        |
+| 0.8354 | 4300  | 0.4531        |
+| 0.8452 | 4350  | 0.4409        |
+| 0.8549 | 4400  | 0.4456        |
+| 0.8646 | 4450  | 0.4312        |
+| 0.8743 | 4500  | 0.4233        |
+| 0.8840 | 4550  | 0.4134        |
+| 0.8937 | 4600  | 0.3193        |
+| 0.9034 | 4650  | 0.2839        |
+| 0.9132 | 4700  | 0.2286        |
+| 0.9229 | 4750  | 0.2572        |
+| 0.9326 | 4800  | 0.2896        |
+| 0.9423 | 4850  | 0.1615        |
+| 0.9520 | 4900  | 0.2984        |
+| 0.9617 | 4950  | 0.1891        |
+| 0.9714 | 5000  | 0.2552        |
+| 0.9812 | 5050  | 0.2165        |
+| 0.9909 | 5100  | 0.2774        |
+| 1.0006 | 5150  | 0.2737        |
+| 1.0103 | 5200  | 0.447         |
+| 1.0200 | 5250  | 0.4317        |
+| 1.0297 | 5300  | 0.3798        |
+| 1.0394 | 5350  | 0.4063        |
+| 1.0492 | 5400  | 0.4231        |
+| 1.0589 | 5450  | 0.4202        |
+| 1.0686 | 5500  | 0.3911        |
+| 1.0783 | 5550  | 0.3807        |
+| 1.0880 | 5600  | 0.3979        |
+| 1.0977 | 5650  | 0.3908        |
+| 1.1074 | 5700  | 0.4167        |
+| 1.1172 | 5750  | 0.3885        |
+| 1.1269 | 5800  | 0.3992        |
+| 1.1366 | 5850  | 0.4102        |
+| 1.1463 | 5900  | 0.3949        |
+| 1.1560 | 5950  | 0.4066        |
+| 1.1657 | 6000  | 0.3871        |
+| 1.1754 | 6050  | 0.3925        |
+| 1.1852 | 6100  | 0.3785        |
+| 1.1949 | 6150  | 0.4529        |
+| 1.2046 | 6200  | 0.4188        |
+| 1.2143 | 6250  | 0.4844        |
+| 1.2240 | 6300  | 0.4171        |
+| 1.2337 | 6350  | 0.4001        |
+| 1.2434 | 6400  | 0.3992        |
+| 1.2532 | 6450  | 0.4167        |
+| 1.2629 | 6500  | 0.4395        |
+| 1.2726 | 6550  | 0.4           |
+| 1.2823 | 6600  | 0.3905        |
+| 1.2920 | 6650  | 0.3769        |
+| 1.3017 | 6700  | 0.3846        |
+| 1.3114 | 6750  | 0.4           |
+| 1.3212 | 6800  | 0.4062        |
+| 1.3309 | 6850  | 0.3972        |
+| 1.3406 | 6900  | 0.3875        |
+| 1.3503 | 6950  | 0.3958        |
+| 1.3600 | 7000  | 0.3843        |
+| 1.3697 | 7050  | 0.4004        |
+| 1.3794 | 7100  | 0.4435        |
+| 1.3892 | 7150  | 0.3856        |
+| 1.3989 | 7200  | 0.3843        |
+| 1.4086 | 7250  | 0.3777        |
+| 1.4183 | 7300  | 0.4103        |
+| 1.4280 | 7350  | 0.3795        |
+| 1.4377 | 7400  | 0.3719        |
+| 1.4474 | 7450  | 0.3938        |
+| 1.4572 | 7500  | 0.4058        |
+| 1.4669 | 7550  | 0.3913        |
+| 1.4766 | 7600  | 0.3992        |
+| 1.4863 | 7650  | 0.3743        |
+| 1.4960 | 7700  | 0.4072        |
+| 1.5057 | 7750  | 0.3788        |
+| 1.5154 | 7800  | 0.3987        |
+| 1.5252 | 7850  | 0.3774        |
+| 1.5349 | 7900  | 0.3803        |
+| 1.5446 | 7950  | 0.3582        |
+| 1.5543 | 8000  | 0.4222        |
+| 1.5640 | 8050  | 0.4001        |
+| 1.5737 | 8100  | 0.3857        |
+| 1.5834 | 8150  | 0.3819        |
+| 1.5932 | 8200  | 0.3643        |
+| 1.6029 | 8250  | 0.3884        |
+| 1.6126 | 8300  | 0.3761        |
+| 1.6223 | 8350  | 0.4295        |
+| 1.6320 | 8400  | 0.4073        |
+| 1.6417 | 8450  | 0.3963        |
+| 1.6514 | 8500  | 0.389         |
+| 1.6612 | 8550  | 0.3677        |
+| 1.6709 | 8600  | 0.4012        |
+| 1.6806 | 8650  | 0.3732        |
+| 1.6903 | 8700  | 0.3793        |
+| 1.7000 | 8750  | 0.3712        |
+| 1.7097 | 8800  | 0.3734        |
+| 1.7194 | 8850  | 0.3895        |
+| 1.7292 | 8900  | 0.3667        |
+| 1.7389 | 8950  | 0.3832        |
+| 1.7486 | 9000  | 0.3842        |
+| 1.7583 | 9050  | 0.3822        |
+| 1.7680 | 9100  | 0.3706        |
+| 1.7777 | 9150  | 0.3699        |
+| 1.7874 | 9200  | 0.3738        |
+| 1.7972 | 9250  | 0.3748        |
+| 1.8069 | 9300  | 0.3911        |
+| 1.8166 | 9350  | 0.366         |
+| 1.8263 | 9400  | 0.3626        |
+| 1.8360 | 9450  | 0.3762        |
+| 1.8457 | 9500  | 0.3711        |
+| 1.8554 | 9550  | 0.3568        |
+| 1.8652 | 9600  | 0.3877        |
+| 1.8749 | 9650  | 0.3744        |
+| 1.8846 | 9700  | 0.3858        |
+| 1.8943 | 9750  | 0.2191        |
+| 1.9040 | 9800  | 0.1622        |
+| 1.9137 | 9850  | 0.13          |
+| 1.9235 | 9900  | 0.359         |
+| 1.9332 | 9950  | 0.1739        |
+| 1.9429 | 10000 | 0.2212        |
+| 1.9526 | 10050 | 0.2445        |
+| 1.9623 | 10100 | 0.2059        |
+| 1.9720 | 10150 | 0.2288        |
+| 1.9817 | 10200 | 0.1985        |
+| 1.9915 | 10250 | 0.182         |
+| 2.0012 | 10300 | 0.2609        |
+| 2.0109 | 10350 | 0.3533        |
+| 2.0206 | 10400 | 0.3322        |
+| 2.0303 | 10450 | 0.3565        |
+| 2.0400 | 10500 | 0.3454        |
+| 2.0497 | 10550 | 0.3623        |
+| 2.0595 | 10600 | 0.3685        |
+| 2.0692 | 10650 | 0.3468        |
+| 2.0789 | 10700 | 0.3448        |
+| 2.0886 | 10750 | 0.3524        |
+| 2.0983 | 10800 | 0.3691        |
+| 2.1080 | 10850 | 0.3505        |
+| 2.1177 | 10900 | 0.3253        |
+| 2.1275 | 10950 | 0.3422        |
+| 2.1372 | 11000 | 0.3321        |
+| 2.1469 | 11050 | 0.3392        |
+| 2.1566 | 11100 | 0.3292        |
+| 2.1663 | 11150 | 0.3572        |
+| 2.1760 | 11200 | 0.3483        |
+| 2.1857 | 11250 | 0.3535        |
+| 2.1955 | 11300 | 0.3559        |
+| 2.2052 | 11350 | 0.3331        |
+| 2.2149 | 11400 | 0.3367        |
+| 2.2246 | 11450 | 0.3538        |
+| 2.2343 | 11500 | 0.3458        |
+| 2.2440 | 11550 | 0.3197        |
+| 2.2537 | 11600 | 0.3587        |
+| 2.2635 | 11650 | 0.3565        |
+| 2.2732 | 11700 | 0.3533        |
+| 2.2829 | 11750 | 0.3191        |
+| 2.2926 | 11800 | 0.3591        |
+| 2.3023 | 11850 | 0.3598        |
+| 2.3120 | 11900 | 0.3495        |
+| 2.3217 | 11950 | 0.353         |
+| 2.3315 | 12000 | 0.3329        |
+| 2.3412 | 12050 | 0.3365        |
+| 2.3509 | 12100 | 0.3246        |
+| 2.3606 | 12150 | 0.3377        |
+| 2.3703 | 12200 | 0.3392        |
+| 2.3800 | 12250 | 0.3546        |
+| 2.3897 | 12300 | 0.3452        |
+| 2.3995 | 12350 | 0.3403        |
+| 2.4092 | 12400 | 0.3473        |
+| 2.4189 | 12450 | 0.336         |
+| 2.4286 | 12500 | 0.3591        |
+| 2.4383 | 12550 | 0.3425        |
+| 2.4480 | 12600 | 0.3293        |
+| 2.4577 | 12650 | 0.3339        |
+| 2.4675 | 12700 | 0.3386        |
+| 2.4772 | 12750 | 0.3335        |
+| 2.4869 | 12800 | 0.3249        |
+| 2.4966 | 12850 | 0.3123        |
+| 2.5063 | 12900 | 0.3182        |
+| 2.5160 | 12950 | 0.3282        |
+| 2.5257 | 13000 | 0.317         |
+| 2.5355 | 13050 | 0.3177        |
+| 2.5452 | 13100 | 0.3075        |
+| 2.5549 | 13150 | 0.3349        |
+| 2.5646 | 13200 | 0.3543        |
+| 2.5743 | 13250 | 0.3228        |
+| 2.5840 | 13300 | 0.3334        |
+| 2.5937 | 13350 | 0.3364        |
+| 2.6035 | 13400 | 0.333         |
+| 2.6132 | 13450 | 0.3633        |
+| 2.6229 | 13500 | 0.3547        |
+| 2.6326 | 13550 | 0.3431        |
+| 2.6423 | 13600 | 0.3265        |
+| 2.6520 | 13650 | 0.3197        |
+| 2.6617 | 13700 | 0.3233        |
+| 2.6715 | 13750 | 0.3293        |
+| 2.6812 | 13800 | 0.3249        |
+| 2.6909 | 13850 | 0.3041        |
+| 2.7006 | 13900 | 0.3612        |
+| 2.7103 | 13950 | 0.3391        |
+| 2.7200 | 14000 | 0.324         |
+| 2.7297 | 14050 | 0.3114        |
+| 2.7395 | 14100 | 0.3365        |
+| 2.7492 | 14150 | 0.2987        |
+| 2.7589 | 14200 | 0.3233        |
+| 2.7686 | 14250 | 0.3221        |
+| 2.7783 | 14300 | 0.3348        |
+| 2.7880 | 14350 | 0.3231        |
+| 2.7977 | 14400 | 0.3407        |
+| 2.8075 | 14450 | 0.3017        |
+| 2.8172 | 14500 | 0.3264        |
+| 2.8269 | 14550 | 0.3349        |
+| 2.8366 | 14600 | 0.3217        |
+| 2.8463 | 14650 | 0.2965        |
+| 2.8560 | 14700 | 0.322         |
+| 2.8657 | 14750 | 0.3195        |
+| 2.8755 | 14800 | 0.3021        |
+| 2.8852 | 14850 | 0.299         |
+| 2.8949 | 14900 | 0.1857        |
+| 2.9046 | 14950 | 0.1839        |
+| 2.9143 | 15000 | 0.1171        |
+| 2.9240 | 15050 | 0.1275        |
+| 2.9337 | 15100 | 0.1814        |
+| 2.9435 | 15150 | 0.1778        |
+| 2.9532 | 15200 | 0.142         |
+| 2.9629 | 15250 | 0.2545        |
+| 2.9726 | 15300 | 0.1202        |
+| 2.9823 | 15350 | 0.132         |
+| 2.9920 | 15400 | 0.154         |
+| 3.0017 | 15450 | 0.2622        |
+| 3.0115 | 15500 | 0.3185        |
+| 3.0212 | 15550 | 0.293         |
+| 3.0309 | 15600 | 0.3164        |
+| 3.0406 | 15650 | 0.2934        |
+| 3.0503 | 15700 | 0.3005        |
+| 3.0600 | 15750 | 0.3017        |
+| 3.0697 | 15800 | 0.2965        |
+| 3.0795 | 15850 | 0.309         |
+| 3.0892 | 15900 | 0.3056        |
+| 3.0989 | 15950 | 0.3318        |
+| 3.1086 | 16000 | 0.3094        |
+| 3.1183 | 16050 | 0.3041        |
+| 3.1280 | 16100 | 0.2981        |
+| 3.1378 | 16150 | 0.316         |
+| 3.1475 | 16200 | 0.3086        |
+| 3.1572 | 16250 | 0.3062        |
+| 3.1669 | 16300 | 0.3069        |
+| 3.1766 | 16350 | 0.312         |
+| 3.1863 | 16400 | 0.3161        |
+| 3.1960 | 16450 | 0.3059        |
+| 3.2058 | 16500 | 0.2899        |
+| 3.2155 | 16550 | 0.312         |
+| 3.2252 | 16600 | 0.3189        |
+| 3.2349 | 16650 | 0.3152        |
+| 3.2446 | 16700 | 0.2998        |
+| 3.2543 | 16750 | 0.301         |
+| 3.2640 | 16800 | 0.3129        |
+| 3.2738 | 16850 | 0.2955        |
+| 3.2835 | 16900 | 0.2923        |
+| 3.2932 | 16950 | 0.3111        |
+| 3.3029 | 17000 | 0.3097        |
+| 3.3126 | 17050 | 0.3045        |
+| 3.3223 | 17100 | 0.296         |
+| 3.3320 | 17150 | 0.3086        |
+| 3.3418 | 17200 | 0.2902        |
+| 3.3515 | 17250 | 0.322         |
+| 3.3612 | 17300 | 0.3105        |
+| 3.3709 | 17350 | 0.3048        |
+| 3.3806 | 17400 | 0.2853        |
+| 3.3903 | 17450 | 0.2795        |
+| 3.4000 | 17500 | 0.2933        |
+| 3.4098 | 17550 | 0.2834        |
+| 3.4195 | 17600 | 0.3           |
+| 3.4292 | 17650 | 0.2998        |
+| 3.4389 | 17700 | 0.2972        |
+| 3.4486 | 17750 | 0.285         |
+| 3.4583 | 17800 | 0.2888        |
+| 3.4680 | 17850 | 0.293         |
+| 3.4778 | 17900 | 0.2941        |
+| 3.4875 | 17950 | 0.3           |
+| 3.4972 | 18000 | 0.3022        |
+| 3.5069 | 18050 | 0.3049        |
+| 3.5166 | 18100 | 0.3067        |
+| 3.5263 | 18150 | 0.2934        |
+| 3.5360 | 18200 | 0.312         |
+| 3.5458 | 18250 | 0.2823        |
+| 3.5555 | 18300 | 0.2746        |
+| 3.5652 | 18350 | 0.2971        |
+| 3.5749 | 18400 | 0.2827        |
+| 3.5846 | 18450 | 0.2718        |
+| 3.5943 | 18500 | 0.2908        |
+| 3.6040 | 18550 | 0.2911        |
+| 3.6138 | 18600 | 0.3008        |
+| 3.6235 | 18650 | 0.3058        |
+| 3.6332 | 18700 | 0.304         |
+| 3.6429 | 18750 | 0.284         |
+| 3.6526 | 18800 | 0.3037        |
+| 3.6623 | 18850 | 0.2768        |
+| 3.6720 | 18900 | 0.3287        |
+| 3.6818 | 18950 | 0.2768        |
+| 3.6915 | 19000 | 0.316         |
+| 3.7012 | 19050 | 0.2786        |
+| 3.7109 | 19100 | 0.2746        |
+| 3.7206 | 19150 | 0.2794        |
+| 3.7303 | 19200 | 0.2869        |
+| 3.7400 | 19250 | 0.2836        |
+| 3.7498 | 19300 | 0.2982        |
+| 3.7595 | 19350 | 0.3143        |
+| 3.7692 | 19400 | 0.2942        |
+| 3.7789 | 19450 | 0.2693        |
+| 3.7886 | 19500 | 0.2894        |
+| 3.7983 | 19550 | 0.3009        |
+| 3.8080 | 19600 | 0.2893        |
+| 3.8178 | 19650 | 0.2915        |
+| 3.8275 | 19700 | 0.2991        |
+| 3.8372 | 19750 | 0.2857        |
+| 3.8469 | 19800 | 0.3028        |
+| 3.8566 | 19850 | 0.3068        |
+| 3.8663 | 19900 | 0.2955        |
+| 3.8760 | 19950 | 0.3119        |
+| 3.8858 | 20000 | 0.3364        |
+| 3.8955 | 20050 | 0.0993        |
+| 3.9052 | 20100 | 0.1208        |
+| 3.9149 | 20150 | 0.1015        |
+| 3.9246 | 20200 | 0.1422        |
+| 3.9343 | 20250 | 0.1879        |
+| 3.9440 | 20300 | 0.1437        |
+| 3.9538 | 20350 | 0.1556        |
+| 3.9635 | 20400 | 0.1279        |
+| 3.9732 | 20450 | 0.1384        |
+| 3.9829 | 20500 | 0.1556        |
+| 3.9926 | 20550 | 0.1508        |
+| 4.0023 | 20600 | 0.1812        |
+| 4.0120 | 20650 | 0.2858        |
+| 4.0218 | 20700 | 0.2807        |
+| 4.0315 | 20750 | 0.3016        |
+| 4.0412 | 20800 | 0.2611        |
+| 4.0509 | 20850 | 0.3031        |
+| 4.0606 | 20900 | 0.2772        |
+| 4.0703 | 20950 | 0.2776        |
+| 4.0800 | 21000 | 0.2556        |
+| 4.0898 | 21050 | 0.2744        |
+| 4.0995 | 21100 | 0.2825        |
+| 4.1092 | 21150 | 0.2664        |
+| 4.1189 | 21200 | 0.2772        |
+| 4.1286 | 21250 | 0.2767        |
+| 4.1383 | 21300 | 0.2562        |
+| 4.1480 | 21350 | 0.256         |
+| 4.1578 | 21400 | 0.2824        |
+| 4.1675 | 21450 | 0.2762        |
+| 4.1772 | 21500 | 0.2766        |
+| 4.1869 | 21550 | 0.291         |
+| 4.1966 | 21600 | 0.2636        |
+| 4.2063 | 21650 | 0.2751        |
+| 4.2160 | 21700 | 0.2739        |
+| 4.2258 | 21750 | 0.2982        |
+| 4.2355 | 21800 | 0.2881        |
+| 4.2452 | 21850 | 0.2687        |
+| 4.2549 | 21900 | 0.2644        |
+| 4.2646 | 21950 | 0.2827        |
+| 4.2743 | 22000 | 0.2591        |
+| 4.2840 | 22050 | 0.2645        |
+| 4.2938 | 22100 | 0.2786        |
+| 4.3035 | 22150 | 0.2693        |
+| 4.3132 | 22200 | 0.2909        |
+| 4.3229 | 22250 | 0.2838        |
+| 4.3326 | 22300 | 0.2901        |
+| 4.3423 | 22350 | 0.2629        |
+| 4.3520 | 22400 | 0.2672        |
+| 4.3618 | 22450 | 0.2962        |
+| 4.3715 | 22500 | 0.2742        |
+| 4.3812 | 22550 | 0.2811        |
+| 4.3909 | 22600 | 0.2639        |
+| 4.4006 | 22650 | 0.244         |
+| 4.4103 | 22700 | 0.2866        |
+| 4.4201 | 22750 | 0.2968        |
+| 4.4298 | 22800 | 0.2828        |
+| 4.4395 | 22850 | 0.2515        |
+| 4.4492 | 22900 | 0.282         |
+| 4.4589 | 22950 | 0.282         |
+| 4.4686 | 23000 | 0.2776        |
+| 4.4783 | 23050 | 0.2795        |
+| 4.4881 | 23100 | 0.2701        |
+| 4.4978 | 23150 | 0.2808        |
+| 4.5075 | 23200 | 0.2651        |
+| 4.5172 | 23250 | 0.2631        |
+| 4.5269 | 23300 | 0.2911        |
+| 4.5366 | 23350 | 0.2615        |
+| 4.5463 | 23400 | 0.2772        |
+| 4.5561 | 23450 | 0.2826        |
+| 4.5658 | 23500 | 0.2797        |
+| 4.5755 | 23550 | 0.2954        |
+| 4.5852 | 23600 | 0.2816        |
+| 4.5949 | 23650 | 0.2889        |
+| 4.6046 | 23700 | 0.2647        |
+| 4.6143 | 23750 | 0.2882        |
+| 4.6241 | 23800 | 0.2709        |
+| 4.6338 | 23850 | 0.2794        |
+| 4.6435 | 23900 | 0.2702        |
+| 4.6532 | 23950 | 0.2527        |
+| 4.6629 | 24000 | 0.2642        |
+| 4.6726 | 24050 | 0.2808        |
+| 4.6823 | 24100 | 0.2764        |
+| 4.6921 | 24150 | 0.2583        |
+| 4.7018 | 24200 | 0.2286        |
+| 4.7115 | 24250 | 0.2707        |
+| 4.7212 | 24300 | 0.2793        |
+| 4.7309 | 24350 | 0.2593        |
+| 4.7406 | 24400 | 0.2779        |
+| 4.7503 | 24450 | 0.3168        |
+| 4.7601 | 24500 | 0.2943        |
+| 4.7698 | 24550 | 0.3078        |
+| 4.7795 | 24600 | 0.2735        |
+| 4.7892 | 24650 | 0.2846        |
+| 4.7989 | 24700 | 0.2571        |
+| 4.8086 | 24750 | 0.2785        |
+| 4.8183 | 24800 | 0.2753        |
+| 4.8281 | 24850 | 0.2943        |
+| 4.8378 | 24900 | 0.264         |
+| 4.8475 | 24950 | 0.2962        |
+| 4.8572 | 25000 | 0.2743        |
+| 4.8669 | 25050 | 0.2748        |
+| 4.8766 | 25100 | 0.3039        |
+| 4.8863 | 25150 | 0.2817        |
+| 4.8961 | 25200 | 0.1467        |
+| 4.9058 | 25250 | 0.1224        |
+| 4.9155 | 25300 | 0.0547        |
+| 4.9252 | 25350 | 0.1329        |
+| 4.9349 | 25400 | 0.086         |
+| 4.9446 | 25450 | 0.1423        |
+| 4.9543 | 25500 | 0.0783        |
+| 4.9641 | 25550 | 0.1377        |
+| 4.9738 | 25600 | 0.0743        |
+| 4.9835 | 25650 | 0.0879        |
+| 4.9932 | 25700 | 0.1108        |
+
+</details>
+
+### Framework Versions
+- Python: 3.11.13
+- Sentence Transformers: 5.0.0
+- Transformers: 4.56.2
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.8.1
+- Datasets: 4.0.0
+- 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",
+}
+```
+
+#### 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}
+}
+```
+
+<!--
+## 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.*
+-->

config.json

@@ -0,0 +1,24 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.56.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "sentence_transformers": "5.0.0",
+    "transformers": "4.56.2",
+    "pytorch": "2.6.0+cu124"
+  },
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9d7f7b4a2cc9abeb1bf0a40343d8708b1a9d8a7a5c16331981ec258b895f0a49
+size 437951328

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 512,
+    "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,66 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}