Add new SentenceTransformer model

1_Pooling/config.json

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

README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:118400
+- loss:TripletLoss
+base_model: DeepChem/ChemBERTa-77M-MLM
+widget:
+- source_sentence: CC(C)C1CCC(C(=O)NC(Cc2ccccc2)C(=O)[O-])CC1
+  sentences:
+  - C[NH2+]CCCC1c2ccccc2C=Cc2ccccc21
+  - COC(=O)NC(C(=O)NC(Cc1ccccc1)C(O)CN(Cc1ccc(-c2ccccn2)cc1)NC(=O)C(NC(=O)OC)C(C)(C)C)C(C)(C)C
+  - CC1C=CC=CCCC=CC=CC=CC=CC(OC2OC(C)C(O)C([NH3+])C2O)CC(O)C(C(=O)[O-])C(O)CC(=O)CC(O)C(O)CCC(O)CC(O)CC(O)CC(=O)OC(C)C(C)C1O
+- source_sentence: C[NH+]1CCCC1Cc1c[nH]c2ccc(CCS(=O)(=O)c3ccccc3)cc12
+  sentences:
+  - CC(C)CC([NH+](C)C)C1(c2ccc(Cl)cc2)CCC1
+  - CC(C)CNCc1ccc(-c2ccccc2S(=O)(=O)N2CCCC2)cc1
+  - CC(Oc1cc(-c2cnn(C3CC[NH2+]CC3)c2)cnc1N)c1c(Cl)ccc(F)c1Cl
+- source_sentence: C[NH+]1C2CCC1CC(OC(c1ccccc1)c1ccccc1)C2
+  sentences:
+  - C[NH2+]C1C(O)C([NH2+]C)C2OC3(O)C(=O)CC(C)OC3OC2C1O
+  - C=CC1(C)CC(OC(=O)CSC2CC3CCC(C2)[NH+]3C)C2(C)C(C)CCC3(CCC(=O)C32)C(C)C1O
+  - CC(C)CC(NC(=O)C(CCc1ccccc1)NC(=O)CN1CCOCC1)C(=O)NC(Cc1ccccc1)C(=O)NC(CC(C)C)C(=O)C1(C)CO1
+- source_sentence: CC(C)CC(NC(=O)C(Cc1ccc2ccccc2c1)NC(=O)C(Cc1ccc(O)cc1)NC(=O)C(CO)NC(=O)C(Cc1c[nH]c2ccccc12)NC(=O)C(Cc1c[nH]cn1)NC(=O)C1CCC(=O)N1)C(=O)NC(CCCNC(N)=[NH2+])C(=O)N1CCCC1C(=O)NCC(N)=O
+  sentences:
+  - C[NH2+]C1CCC(c2ccc(Cl)c(Cl)c2)c2ccccc21
+  - C=C1CC2CCC34CC5OC6C(OC7CCC(CC(=O)CC8C(CC9OC(CCC1O2)CC(C)C9=C)OC(CC(O)CN)C8OC)OC7C6O3)C5O4
+  - C[N+]1(C)CCC(=C(c2ccccc2)c2ccccc2)CC1
+- source_sentence: CON=C(C(=O)NC1C(=O)N2C(C(=O)[O-])=C(C[N+]3(C)CCCC3)CSC12)c1csc(N)n1
+  sentences:
+  - CC1CNc2c(cccc2S(=O)(=O)NC(CCC[NH+]=C(N)N)C(=O)N2CCC(C)CC2C(=O)[O-])C1
+  - CC1C=CC=CCCC=CC=CC=CC=CC(OC2OC(C)C(O)C([NH3+])C2O)CC(O)C(C(=O)[O-])C(O)CC(=O)CC(O)C(O)CCC(O)CC(O)CC(O)CC(=O)OC(C)C(C)C1O
+  - CC(C)C1(C(=O)NC2CC(=O)OC2(O)CF)CC(c2nccc3ccccc23)=NO1
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy
+model-index:
+- name: SentenceTransformer based on DeepChem/ChemBERTa-77M-MLM
+  results:
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: all dev
+      type: all-dev
+    metrics:
+    - type: cosine_accuracy
+      value: 0.7135134935379028
+      name: Cosine Accuracy
+---
+
+# SentenceTransformer based on DeepChem/ChemBERTa-77M-MLM
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [DeepChem/ChemBERTa-77M-MLM](https://huggingface.co/DeepChem/ChemBERTa-77M-MLM). 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:** [DeepChem/ChemBERTa-77M-MLM](https://huggingface.co/DeepChem/ChemBERTa-77M-MLM) <!-- at revision ed8a5374f2024ec8da53760af91a33fb8f6a15ff -->
+- **Maximum Sequence Length:** 512 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': 512, 'do_lower_case': False}) with Transformer model: RobertaModel 
+  (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})
+)
+```
+
+## 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("HassanCS/chemBERTa-tuned-on-ClinTox-using-triplet-loss")
+# Run inference
+sentences = [
+    'CON=C(C(=O)NC1C(=O)N2C(C(=O)[O-])=C(C[N+]3(C)CCCC3)CSC12)c1csc(N)n1',
+    'CC1CNc2c(cccc2S(=O)(=O)NC(CCC[NH+]=C(N)N)C(=O)N2CCC(C)CC2C(=O)[O-])C1',
+    'CC(C)C1(C(=O)NC2CC(=O)OC2(O)CF)CC(c2nccc3ccccc23)=NO1',
+]
+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.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Triplet
+
+* Dataset: `all-dev`
+* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| **cosine_accuracy** | **0.7135** |
+
+<!--
+## 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: 118,400 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: 20 tokens</li><li>mean: 33.0 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 47.34 tokens</li><li>max: 212 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 53.88 tokens</li><li>max: 212 tokens</li></ul> |
+* Samples:
+  | anchor                                                 | positive                                                                                     | negative                                             |
+  |:-------------------------------------------------------|:---------------------------------------------------------------------------------------------|:-----------------------------------------------------|
+  | <code>CC(C)CC(NC(=O)CNC(=O)c1cc(Cl)ccc1Cl)B(O)O</code> | <code>CC(=O)OC1CCC2(C)C(=CCC3C2CCC2(C)C(c4cccnc4)=CCC32)C1</code>                            | <code>CCOC(=O)c1ncn2c1CN(C)C(=O)c1cc(F)ccc1-2</code> |
+  | <code>CC(C)CC(NC(=O)CNC(=O)c1cc(Cl)ccc1Cl)B(O)O</code> | <code>COc1ccc(C(CN(C)C)C2(O)CCCCC2)cc1</code>                                                | <code>C[NH2+]C1(C)C2CCC(C2)C1(C)C</code>             |
+  | <code>CC(C)CC(NC(=O)CNC(=O)c1cc(Cl)ccc1Cl)B(O)O</code> | <code>CNC(=O)c1cc(Oc2ccc(NC(=O)Nc3ccc(Cl)c(C(F)(F)F)c3)cc2)ccn1.Cc1ccc(S(=O)(=O)O)cc1</code> | <code>Nc1ncnc2c1ncn2C1OC(CO)C(O)C1O</code>           |
+* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
+  ```json
+  {
+      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
+      "triplet_margin": 5
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+* Size: 1,480 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: 18 tokens</li><li>mean: 54.07 tokens</li><li>max: 169 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 60.4 tokens</li><li>max: 244 tokens</li></ul> | <ul><li>min: 30 tokens</li><li>mean: 71.25 tokens</li><li>max: 141 tokens</li></ul> |
+* Samples:
+  | anchor                                                                  | positive                                                    | negative                                                                 |
+  |:------------------------------------------------------------------------|:------------------------------------------------------------|:-------------------------------------------------------------------------|
+  | <code>CC(C)OC(=O)CCCC=CCC1C(O)CC(O)C1C=CC(O)COc1cccc(C(F)(F)F)c1</code> | <code>CC12CCCCCC(Cc3ccc(O)cc31)C2[NH3+]</code>              | <code>CC(C)C(CN1CCC(C)(c2cccc(O)c2)C(C)C1)NC(=O)C1Cc2ccc(O)cc2CN1</code> |
+  | <code>CC(C)OC(=O)CCCC=CCC1C(O)CC(O)C1C=CC(O)COc1cccc(C(F)(F)F)c1</code> | <code>COc1cc2c(cc1OC)C1CC(=O)C(CC(C)C)C[NH+]1CC2</code>     | <code>CC(C)C(CN1CCC(C)(c2cccc(O)c2)C(C)C1)NC(=O)C1Cc2ccc(O)cc2CN1</code> |
+  | <code>CC(C)OC(=O)CCCC=CCC1C(O)CC(O)C1C=CC(O)COc1cccc(C(F)(F)F)c1</code> | <code>C[NH+](C)CCC=C1c2ccccc2COc2ccc(CC(=O)[O-])cc21</code> | <code>CC(C)C1(C(=O)NC2CC(=O)OC2(O)CF)CC(c2nccc3ccccc23)=NO1</code>       |
+* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
+  ```json
+  {
+      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
+      "triplet_margin": 5
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 5
+- `warmup_ratio`: 0.1
+- `fp16`: 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`: 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`: 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`: 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
+- `dispatch_batches`: None
+- `split_batches`: 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`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step  | Training Loss | Validation Loss | all-dev_cosine_accuracy |
+|:------:|:-----:|:-------------:|:---------------:|:-----------------------:|
+| 0.0676 | 500   | 5.0821        | 5.1737          | 0.4047                  |
+| 0.1351 | 1000  | 4.9869        | 5.1766          | 0.4230                  |
+| 0.2027 | 1500  | 4.5562        | 4.9102          | 0.5345                  |
+| 0.2703 | 2000  | 3.2364        | 4.3712          | 0.6534                  |
+| 0.3378 | 2500  | 2.0738        | 4.0704          | 0.6736                  |
+| 0.4054 | 3000  | 1.4239        | 4.0200          | 0.6635                  |
+| 0.4730 | 3500  | 1.1578        | 3.7202          | 0.6791                  |
+| 0.5405 | 4000  | 0.9669        | 3.7197          | 0.6831                  |
+| 0.6081 | 4500  | 0.714         | 3.8818          | 0.6547                  |
+| 0.6757 | 5000  | 0.5359        | 4.0987          | 0.6243                  |
+| 0.7432 | 5500  | 0.5663        | 3.8127          | 0.6500                  |
+| 0.8108 | 6000  | 0.4827        | 3.8346          | 0.6676                  |
+| 0.8784 | 6500  | 0.4758        | 3.8333          | 0.6507                  |
+| 0.9459 | 7000  | 0.4759        | 3.6872          | 0.6912                  |
+| 1.0135 | 7500  | 0.4651        | 3.7229          | 0.6831                  |
+| 1.0811 | 8000  | 0.4739        | 3.8041          | 0.6662                  |
+| 1.1486 | 8500  | 0.4458        | 3.8235          | 0.6703                  |
+| 1.2162 | 9000  | 0.4189        | 3.7957          | 0.6716                  |
+| 1.2838 | 9500  | 0.4504        | 3.7422          | 0.6784                  |
+| 1.3514 | 10000 | 0.413         | 3.7588          | 0.6770                  |
+| 1.4189 | 10500 | 0.3808        | 3.9750          | 0.6615                  |
+| 1.4865 | 11000 | 0.3853        | 3.7417          | 0.6953                  |
+| 1.5541 | 11500 | 0.379         | 3.7319          | 0.6993                  |
+| 1.6216 | 12000 | 0.429         | 3.5620          | 0.7209                  |
+| 1.6892 | 12500 | 0.3735        | 3.6900          | 0.7020                  |
+| 1.7568 | 13000 | 0.3908        | 3.8182          | 0.6932                  |
+| 1.8243 | 13500 | 0.3848        | 3.7228          | 0.7101                  |
+| 1.8919 | 14000 | 0.3777        | 3.6604          | 0.7149                  |
+| 1.9595 | 14500 | 0.3912        | 3.7849          | 0.6946                  |
+| 2.0269 | 15000 | 0.3282        | 3.8607          | 0.7014                  |
+| 2.0945 | 15500 | 0.3324        | 3.8573          | 0.6953                  |
+| 2.1620 | 16000 | 0.3852        | 3.9420          | 0.7000                  |
+| 2.2296 | 16500 | 0.3633        | 3.7928          | 0.7189                  |
+| 2.2972 | 17000 | 0.3493        | 3.8217          | 0.7216                  |
+| 2.3647 | 17500 | 0.3554        | 3.8546          | 0.6993                  |
+| 2.4323 | 18000 | 0.3363        | 3.7764          | 0.6993                  |
+| 2.4999 | 18500 | 0.377         | 3.8224          | 0.6959                  |
+| 2.5674 | 19000 | 0.3569        | 3.8376          | 0.7155                  |
+| 2.635  | 19500 | 0.3414        | 4.0017          | 0.7034                  |
+| 2.7026 | 20000 | 0.3567        | 3.7405          | 0.7135                  |
+| 2.7701 | 20500 | 0.3524        | 3.9446          | 0.7189                  |
+| 2.8377 | 21000 | 0.3347        | 3.8140          | 0.7169                  |
+| 2.9053 | 21500 | 0.3458        | 4.0700          | 0.7088                  |
+| 2.9728 | 22000 | 0.3632        | 3.7930          | 0.7081                  |
+| 3.0404 | 22500 | 0.3496        | 3.9884          | 0.7236                  |
+| 3.1080 | 23000 | 0.3426        | 3.7102          | 0.7155                  |
+| 3.1755 | 23500 | 0.3579        | 3.9201          | 0.7135                  |
+| 3.2431 | 24000 | 0.3553        | 4.2237          | 0.7270                  |
+| 3.3107 | 24500 | 0.345         | 3.8090          | 0.7189                  |
+| 3.3782 | 25000 | 0.3475        | 3.7802          | 0.7284                  |
+| 3.4458 | 25500 | 0.3326        | 3.7549          | 0.7250                  |
+| 3.5134 | 26000 | 0.3228        | 3.6717          | 0.7216                  |
+| 3.5809 | 26500 | 0.3311        | 3.8241          | 0.7155                  |
+| 3.6485 | 27000 | 0.3215        | 3.8151          | 0.7142                  |
+| 3.7161 | 27500 | 0.3534        | 3.8639          | 0.7149                  |
+| 3.7836 | 28000 | 0.3369        | 4.0947          | 0.7101                  |
+| 3.8512 | 28500 | 0.3229        | 4.0495          | 0.7101                  |
+| 3.9188 | 29000 | 0.3442        | 4.0408          | 0.7169                  |
+| 3.9864 | 29500 | 0.3059        | 3.9493          | 0.6959                  |
+| 4.0538 | 30000 | 0.3349        | 4.0431          | 0.7108                  |
+| 4.1214 | 30500 | 0.3266        | 4.0224          | 0.7189                  |
+| 4.1889 | 31000 | 0.3501        | 3.9502          | 0.7169                  |
+| 4.2565 | 31500 | 0.3676        | 3.8903          | 0.7196                  |
+| 4.3241 | 32000 | 0.3191        | 3.7994          | 0.7162                  |
+| 4.3916 | 32500 | 0.3317        | 3.7889          | 0.7182                  |
+| 4.4592 | 33000 | 0.3304        | 3.8661          | 0.7108                  |
+| 4.5268 | 33500 | 0.3332        | 3.8822          | 0.7115                  |
+| 4.5943 | 34000 | 0.3435        | 3.7945          | 0.7088                  |
+| 4.6619 | 34500 | 0.317         | 3.8721          | 0.7243                  |
+| 4.7295 | 35000 | 0.3038        | 3.8615          | 0.7209                  |
+| 4.7970 | 35500 | 0.3093        | 3.8360          | 0.7162                  |
+| 4.8646 | 36000 | 0.3309        | 3.8277          | 0.7155                  |
+| 4.9322 | 36500 | 0.3378        | 3.7988          | 0.7128                  |
+| 4.9997 | 37000 | 0.311         | 3.8015          | 0.7135                  |
+
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 3.4.1
+- Transformers: 4.47.1
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.2.1
+- Datasets: 3.2.0
+- Tokenizers: 0.21.0
+
+## 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",
+}
+```
+
+#### TripletLoss
+```bibtex
+@misc{hermans2017defense,
+    title={In Defense of the Triplet Loss for Person Re-Identification},
+    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
+    year={2017},
+    eprint={1703.07737},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+```
+
+<!--
+## 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.*
+-->

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