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1_Pooling/config.json

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+{
+    "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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+---
+language:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:8407159
+- loss:CoSENTLoss
+base_model: sentence-transformers/all-MiniLM-L6-v2
+widget:
+- source_sentence: mixed berry milk shake
+  sentences:
+  - crystal infinity ring
+  - frozen green peas
+  - nongreasy sun stick
+- source_sentence: jumper
+  sentences:
+  - crepes skillet
+  - halonace tablets
+  - patterned cushion
+- source_sentence: chilli cracker
+  sentences:
+  - soy proteins hair mist
+  - comfort outfit
+  - skillet
+- source_sentence: printed slipper
+  sentences:
+  - smoothing body cream
+  - keychains storage hooks
+  - pants
+- source_sentence: relaxed fit swim shorts
+  sentences:
+  - elasticated edges swimsuit
+  - mustard square cushion
+  - calcium boost nail polish
+datasets:
+- KhaledReda/pairs_three_scores_v11_tag_sim
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# all-MiniLM-L6-v13-pair_score
+
+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) on the [pairs_three_scores_v11_tag_sim](https://huggingface.co/datasets/KhaledReda/pairs_three_scores_v11_tag_sim) dataset. 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:**
+    - [pairs_three_scores_v11_tag_sim](https://huggingface.co/datasets/KhaledReda/pairs_three_scores_v11_tag_sim)
+- **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': 256, 'do_lower_case': False, 'architecture': '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 = [
+    'relaxed fit swim shorts',
+    'elasticated edges swimsuit',
+    'mustard square cushion',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.8868, 0.6624],
+#         [0.8868, 1.0000, 0.6654],
+#         [0.6624, 0.6654, 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
+
+#### pairs_three_scores_v11_tag_sim
+
+* Dataset: [pairs_three_scores_v11_tag_sim](https://huggingface.co/datasets/KhaledReda/pairs_three_scores_v11_tag_sim) at [e98a5bf](https://huggingface.co/datasets/KhaledReda/pairs_three_scores_v11_tag_sim/tree/e98a5bf2c08155f512fbaa781b54635acf2f56b3)
+* Size: 8,407,159 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                        | sentence2                                                                        | score                                                            |
+  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                           | float                                                            |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 5.71 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.94 tokens</li><li>max: 84 tokens</li></ul> | <ul><li>min: 0.15</li><li>mean: 0.36</li><li>max: 0.96</li></ul> |
+* Samples:
+  | sentence1                            | sentence2                                | score             |
+  |:-------------------------------------|:-----------------------------------------|:------------------|
+  | <code>chicken mushroom pasta</code>  | <code>rosemary essential hair oil</code> | <code>0.19</code> |
+  | <code>deli</code>                    | <code>mozzarella pacman</code>           | <code>0.25</code> |
+  | <code>tea tree oil face cream</code> | <code>all skin types cream</code>        | <code>0.33</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### pairs_three_scores_v11_tag_sim
+
+* Dataset: [pairs_three_scores_v11_tag_sim](https://huggingface.co/datasets/KhaledReda/pairs_three_scores_v11_tag_sim) at [e98a5bf](https://huggingface.co/datasets/KhaledReda/pairs_three_scores_v11_tag_sim/tree/e98a5bf2c08155f512fbaa781b54635acf2f56b3)
+* Size: 42,248 evaluation samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                         | sentence2                                                                        | score                                                            |
+  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                           | float                                                            |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 5.69 tokens</li><li>max: 115 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.91 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 0.14</li><li>mean: 0.38</li><li>max: 0.93</li></ul> |
+* Samples:
+  | sentence1                                | sentence2                                 | score             |
+  |:-----------------------------------------|:------------------------------------------|:------------------|
+  | <code>comeback sauce sandwiches</code>   | <code>lemon dill sandwich</code>          | <code>0.82</code> |
+  | <code>crossfit tiers storage rack</code> | <code>garage gear parallel</code>         | <code>0.78</code> |
+  | <code>shake</code>                       | <code>toast with balsamic dressing</code> | <code>0.23</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `fp16`: True
+
+#### 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`: 128
+- `per_device_eval_batch_size`: 128
+- `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`: 1
+- `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
+- `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`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step  | Training Loss |
+|:------:|:-----:|:-------------:|
+| 0.0015 | 100   | 11.8209       |
+| 0.0030 | 200   | 11.5402       |
+| 0.0046 | 300   | 11.2817       |
+| 0.0061 | 400   | 11.1501       |
+| 0.0076 | 500   | 10.5829       |
+| 0.0091 | 600   | 10.0617       |
+| 0.0107 | 700   | 9.6697        |
+| 0.0122 | 800   | 9.3381        |
+| 0.0137 | 900   | 8.9682        |
+| 0.0152 | 1000  | 8.7555        |
+| 0.0167 | 1100  | 8.6119        |
+| 0.0183 | 1200  | 8.5628        |
+| 0.0198 | 1300  | 8.5158        |
+| 0.0213 | 1400  | 8.4803        |
+| 0.0228 | 1500  | 8.4599        |
+| 0.0244 | 1600  | 8.4506        |
+| 0.0259 | 1700  | 8.4444        |
+| 0.0274 | 1800  | 8.4166        |
+| 0.0289 | 1900  | 8.4118        |
+| 0.0305 | 2000  | 8.3848        |
+| 0.0320 | 2100  | 8.3781        |
+| 0.0335 | 2200  | 8.3782        |
+| 0.0350 | 2300  | 8.3495        |
+| 0.0365 | 2400  | 8.3462        |
+| 0.0381 | 2500  | 8.3582        |
+| 0.0396 | 2600  | 8.3337        |
+| 0.0411 | 2700  | 8.3263        |
+| 0.0426 | 2800  | 8.3261        |
+| 0.0442 | 2900  | 8.2994        |
+| 0.0457 | 3000  | 8.3027        |
+| 0.0472 | 3100  | 8.3071        |
+| 0.0487 | 3200  | 8.3062        |
+| 0.0502 | 3300  | 8.2753        |
+| 0.0518 | 3400  | 8.2743        |
+| 0.0533 | 3500  | 8.2805        |
+| 0.0548 | 3600  | 8.2679        |
+| 0.0563 | 3700  | 8.2735        |
+| 0.0579 | 3800  | 8.2669        |
+| 0.0594 | 3900  | 8.2665        |
+| 0.0609 | 4000  | 8.2463        |
+| 0.0624 | 4100  | 8.2464        |
+| 0.0639 | 4200  | 8.2505        |
+| 0.0655 | 4300  | 8.2347        |
+| 0.0670 | 4400  | 8.2356        |
+| 0.0685 | 4500  | 8.2143        |
+| 0.0700 | 4600  | 8.2157        |
+| 0.0716 | 4700  | 8.2123        |
+| 0.0731 | 4800  | 8.2186        |
+| 0.0746 | 4900  | 8.2071        |
+| 0.0761 | 5000  | 8.2004        |
+| 0.0776 | 5100  | 8.2007        |
+| 0.0792 | 5200  | 8.2099        |
+| 0.0807 | 5300  | 8.1938        |
+| 0.0822 | 5400  | 8.1945        |
+| 0.0837 | 5500  | 8.1857        |
+| 0.0853 | 5600  | 8.1857        |
+| 0.0868 | 5700  | 8.183         |
+| 0.0883 | 5800  | 8.1942        |
+| 0.0898 | 5900  | 8.1603        |
+| 0.0914 | 6000  | 8.1564        |
+| 0.0929 | 6100  | 8.1644        |
+| 0.0944 | 6200  | 8.1707        |
+| 0.0959 | 6300  | 8.1625        |
+| 0.0974 | 6400  | 8.1428        |
+| 0.0990 | 6500  | 8.1556        |
+| 0.1005 | 6600  | 8.1423        |
+| 0.1020 | 6700  | 8.1434        |
+| 0.1035 | 6800  | 8.14          |
+| 0.1051 | 6900  | 8.15          |
+| 0.1066 | 7000  | 8.1369        |
+| 0.1081 | 7100  | 8.1349        |
+| 0.1096 | 7200  | 8.1414        |
+| 0.1111 | 7300  | 8.1175        |
+| 0.1127 | 7400  | 8.1144        |
+| 0.1142 | 7500  | 8.1202        |
+| 0.1157 | 7600  | 8.0978        |
+| 0.1172 | 7700  | 8.1213        |
+| 0.1188 | 7800  | 8.0999        |
+| 0.1203 | 7900  | 8.1001        |
+| 0.1218 | 8000  | 8.0883        |
+| 0.1233 | 8100  | 8.1111        |
+| 0.1248 | 8200  | 8.1003        |
+| 0.1264 | 8300  | 8.0971        |
+| 0.1279 | 8400  | 8.1177        |
+| 0.1294 | 8500  | 8.099         |
+| 0.1309 | 8600  | 8.1015        |
+| 0.1325 | 8700  | 8.1006        |
+| 0.1340 | 8800  | 8.0826        |
+| 0.1355 | 8900  | 8.0896        |
+| 0.1370 | 9000  | 8.072         |
+| 0.1385 | 9100  | 8.0749        |
+| 0.1401 | 9200  | 8.0864        |
+| 0.1416 | 9300  | 8.0913        |
+| 0.1431 | 9400  | 8.0794        |
+| 0.1446 | 9500  | 8.0693        |
+| 0.1462 | 9600  | 8.0665        |
+| 0.1477 | 9700  | 8.0698        |
+| 0.1492 | 9800  | 8.0788        |
+| 0.1507 | 9900  | 8.062         |
+| 0.1523 | 10000 | 8.0552        |
+| 0.1538 | 10100 | 8.0699        |
+| 0.1553 | 10200 | 8.0528        |
+| 0.1568 | 10300 | 8.0469        |
+| 0.1583 | 10400 | 8.0657        |
+| 0.1599 | 10500 | 8.0533        |
+| 0.1614 | 10600 | 8.0503        |
+| 0.1629 | 10700 | 8.0665        |
+| 0.1644 | 10800 | 8.0383        |
+| 0.1660 | 10900 | 8.0477        |
+| 0.1675 | 11000 | 8.0487        |
+| 0.1690 | 11100 | 8.0564        |
+| 0.1705 | 11200 | 8.0657        |
+| 0.1720 | 11300 | 8.0477        |
+| 0.1736 | 11400 | 8.0444        |
+| 0.1751 | 11500 | 8.0469        |
+| 0.1766 | 11600 | 8.0384        |
+| 0.1781 | 11700 | 8.0414        |
+| 0.1797 | 11800 | 8.0446        |
+| 0.1812 | 11900 | 8.0492        |
+| 0.1827 | 12000 | 8.0391        |
+| 0.1842 | 12100 | 8.0234        |
+| 0.1857 | 12200 | 8.0256        |
+| 0.1873 | 12300 | 8.0346        |
+| 0.1888 | 12400 | 8.0245        |
+| 0.1903 | 12500 | 8.0185        |
+| 0.1918 | 12600 | 8.0225        |
+| 0.1934 | 12700 | 8.0267        |
+| 0.1949 | 12800 | 8.0468        |
+| 0.1964 | 12900 | 8.0195        |
+| 0.1979 | 13000 | 8.0293        |
+| 0.1994 | 13100 | 8.0132        |
+| 0.2010 | 13200 | 8.029         |
+| 0.2025 | 13300 | 8.0177        |
+| 0.2040 | 13400 | 7.9961        |
+| 0.2055 | 13500 | 8.0149        |
+| 0.2071 | 13600 | 8.0102        |
+| 0.2086 | 13700 | 8.0201        |
+| 0.2101 | 13800 | 8.0256        |
+| 0.2116 | 13900 | 8.0067        |
+| 0.2132 | 14000 | 8.0105        |
+| 0.2147 | 14100 | 8.0077        |
+| 0.2162 | 14200 | 8.0151        |
+| 0.2177 | 14300 | 8.0208        |
+| 0.2192 | 14400 | 7.9954        |
+| 0.2208 | 14500 | 8.0184        |
+| 0.2223 | 14600 | 7.9915        |
+| 0.2238 | 14700 | 8.0013        |
+| 0.2253 | 14800 | 8.0177        |
+| 0.2269 | 14900 | 7.9963        |
+| 0.2284 | 15000 | 8.0014        |
+| 0.2299 | 15100 | 8.0038        |
+| 0.2314 | 15200 | 7.9831        |
+| 0.2329 | 15300 | 7.9983        |
+| 0.2345 | 15400 | 7.9953        |
+| 0.2360 | 15500 | 7.9829        |
+| 0.2375 | 15600 | 7.9888        |
+| 0.2390 | 15700 | 7.9763        |
+| 0.2406 | 15800 | 7.9822        |
+| 0.2421 | 15900 | 7.9795        |
+| 0.2436 | 16000 | 7.9858        |
+| 0.2451 | 16100 | 7.9737        |
+| 0.2466 | 16200 | 7.9821        |
+| 0.2482 | 16300 | 7.9793        |
+| 0.2497 | 16400 | 7.9683        |
+| 0.2512 | 16500 | 7.9785        |
+| 0.2527 | 16600 | 7.9766        |
+| 0.2543 | 16700 | 7.979         |
+| 0.2558 | 16800 | 7.977         |
+| 0.2573 | 16900 | 7.9742        |
+| 0.2588 | 17000 | 7.9824        |
+| 0.2603 | 17100 | 7.96          |
+| 0.2619 | 17200 | 7.9828        |
+| 0.2634 | 17300 | 7.9696        |
+| 0.2649 | 17400 | 7.979         |
+| 0.2664 | 17500 | 7.9837        |
+| 0.2680 | 17600 | 7.955         |
+| 0.2695 | 17700 | 7.9561        |
+| 0.2710 | 17800 | 7.9899        |
+| 0.2725 | 17900 | 7.9699        |
+| 0.2741 | 18000 | 7.9849        |
+| 0.2756 | 18100 | 7.9622        |
+| 0.2771 | 18200 | 7.9561        |
+| 0.2786 | 18300 | 7.976         |
+| 0.2801 | 18400 | 7.9805        |
+| 0.2817 | 18500 | 7.9639        |
+| 0.2832 | 18600 | 7.9533        |
+| 0.2847 | 18700 | 7.972         |
+| 0.2862 | 18800 | 7.9847        |
+| 0.2878 | 18900 | 7.9502        |
+| 0.2893 | 19000 | 7.9681        |
+| 0.2908 | 19100 | 7.9574        |
+| 0.2923 | 19200 | 7.9697        |
+| 0.2938 | 19300 | 7.9639        |
+| 0.2954 | 19400 | 7.955         |
+| 0.2969 | 19500 | 7.9647        |
+| 0.2984 | 19600 | 7.9565        |
+| 0.2999 | 19700 | 7.9494        |
+| 0.3015 | 19800 | 7.9708        |
+| 0.3030 | 19900 | 7.9599        |
+| 0.3045 | 20000 | 7.9781        |
+| 0.3060 | 20100 | 7.9363        |
+| 0.3075 | 20200 | 7.9599        |
+| 0.3091 | 20300 | 7.9311        |
+| 0.3106 | 20400 | 7.9446        |
+| 0.3121 | 20500 | 7.9482        |
+| 0.3136 | 20600 | 7.9529        |
+| 0.3152 | 20700 | 7.9624        |
+| 0.3167 | 20800 | 7.9534        |
+| 0.3182 | 20900 | 7.9588        |
+| 0.3197 | 21000 | 7.9606        |
+| 0.3212 | 21100 | 7.9268        |
+| 0.3228 | 21200 | 7.9501        |
+| 0.3243 | 21300 | 7.9346        |
+| 0.3258 | 21400 | 7.9411        |
+| 0.3273 | 21500 | 7.9331        |
+| 0.3289 | 21600 | 7.9612        |
+| 0.3304 | 21700 | 7.9609        |
+| 0.3319 | 21800 | 7.9322        |
+| 0.3334 | 21900 | 7.9416        |
+| 0.3350 | 22000 | 7.9288        |
+| 0.3365 | 22100 | 7.9436        |
+| 0.3380 | 22200 | 7.9382        |
+| 0.3395 | 22300 | 7.9259        |
+| 0.3410 | 22400 | 7.9265        |
+| 0.3426 | 22500 | 7.9275        |
+| 0.3441 | 22600 | 7.9568        |
+| 0.3456 | 22700 | 7.9347        |
+| 0.3471 | 22800 | 7.9205        |
+| 0.3487 | 22900 | 7.9319        |
+| 0.3502 | 23000 | 7.9266        |
+| 0.3517 | 23100 | 7.9435        |
+| 0.3532 | 23200 | 7.9404        |
+| 0.3547 | 23300 | 7.9327        |
+| 0.3563 | 23400 | 7.9312        |
+| 0.3578 | 23500 | 7.93          |
+| 0.3593 | 23600 | 7.916         |
+| 0.3608 | 23700 | 7.9342        |
+| 0.3624 | 23800 | 7.9371        |
+| 0.3639 | 23900 | 7.917         |
+| 0.3654 | 24000 | 7.9196        |
+| 0.3669 | 24100 | 7.934         |
+| 0.3684 | 24200 | 7.929         |
+| 0.3700 | 24300 | 7.9386        |
+| 0.3715 | 24400 | 7.9194        |
+| 0.3730 | 24500 | 7.9228        |
+| 0.3745 | 24600 | 7.9261        |
+| 0.3761 | 24700 | 7.9218        |
+| 0.3776 | 24800 | 7.9048        |
+| 0.3791 | 24900 | 7.9264        |
+| 0.3806 | 25000 | 7.9198        |
+| 0.3822 | 25100 | 7.9206        |
+| 0.3837 | 25200 | 7.9159        |
+| 0.3852 | 25300 | 7.9106        |
+| 0.3867 | 25400 | 7.905         |
+| 0.3882 | 25500 | 7.9215        |
+| 0.3898 | 25600 | 7.9186        |
+| 0.3913 | 25700 | 7.9055        |
+| 0.3928 | 25800 | 7.9032        |
+| 0.3943 | 25900 | 7.9094        |
+| 0.3959 | 26000 | 7.8977        |
+| 0.3974 | 26100 | 7.9013        |
+| 0.3989 | 26200 | 7.918         |
+| 0.4004 | 26300 | 7.9182        |
+| 0.4019 | 26400 | 7.9105        |
+| 0.4035 | 26500 | 7.9071        |
+| 0.4050 | 26600 | 7.9253        |
+| 0.4065 | 26700 | 7.9091        |
+| 0.4080 | 26800 | 7.9196        |
+| 0.4096 | 26900 | 7.9094        |
+| 0.4111 | 27000 | 7.9229        |
+| 0.4126 | 27100 | 7.911         |
+| 0.4141 | 27200 | 7.8899        |
+| 0.4156 | 27300 | 7.9316        |
+| 0.4172 | 27400 | 7.8894        |
+| 0.4187 | 27500 | 7.9143        |
+| 0.4202 | 27600 | 7.9046        |
+| 0.4217 | 27700 | 7.8977        |
+| 0.4233 | 27800 | 7.8756        |
+| 0.4248 | 27900 | 7.8881        |
+| 0.4263 | 28000 | 7.9026        |
+| 0.4278 | 28100 | 7.9071        |
+| 0.4293 | 28200 | 7.9115        |
+| 0.4309 | 28300 | 7.9058        |
+| 0.4324 | 28400 | 7.889         |
+| 0.4339 | 28500 | 7.8942        |
+| 0.4354 | 28600 | 7.8969        |
+| 0.4370 | 28700 | 7.9029        |
+| 0.4385 | 28800 | 7.8911        |
+| 0.4400 | 28900 | 7.8799        |
+| 0.4415 | 29000 | 7.8743        |
+| 0.4431 | 29100 | 7.9117        |
+| 0.4446 | 29200 | 7.8922        |
+| 0.4461 | 29300 | 7.9221        |
+| 0.4476 | 29400 | 7.8975        |
+| 0.4491 | 29500 | 7.9151        |
+| 0.4507 | 29600 | 7.8861        |
+| 0.4522 | 29700 | 7.9109        |
+| 0.4537 | 29800 | 7.8892        |
+| 0.4552 | 29900 | 7.9072        |
+| 0.4568 | 30000 | 7.9004        |
+| 0.4583 | 30100 | 7.8736        |
+| 0.4598 | 30200 | 7.9009        |
+| 0.4613 | 30300 | 7.9058        |
+| 0.4628 | 30400 | 7.8926        |
+| 0.4644 | 30500 | 7.9111        |
+| 0.4659 | 30600 | 7.8922        |
+| 0.4674 | 30700 | 7.9212        |
+| 0.4689 | 30800 | 7.8591        |
+| 0.4705 | 30900 | 7.8885        |
+| 0.4720 | 31000 | 7.9038        |
+| 0.4735 | 31100 | 7.8983        |
+| 0.4750 | 31200 | 7.8894        |
+| 0.4765 | 31300 | 7.8918        |
+| 0.4781 | 31400 | 7.8758        |
+| 0.4796 | 31500 | 7.8818        |
+| 0.4811 | 31600 | 7.8897        |
+| 0.4826 | 31700 | 7.8722        |
+| 0.4842 | 31800 | 7.8683        |
+| 0.4857 | 31900 | 7.8811        |
+| 0.4872 | 32000 | 7.8735        |
+| 0.4887 | 32100 | 7.8972        |
+| 0.4902 | 32200 | 7.8855        |
+| 0.4918 | 32300 | 7.8977        |
+| 0.4933 | 32400 | 7.8635        |
+| 0.4948 | 32500 | 7.8849        |
+| 0.4963 | 32600 | 7.8745        |
+| 0.4979 | 32700 | 7.8924        |
+| 0.4994 | 32800 | 7.8666        |
+| 0.5009 | 32900 | 7.8872        |
+| 0.5024 | 33000 | 7.8965        |
+| 0.5040 | 33100 | 7.8705        |
+| 0.5055 | 33200 | 7.8926        |
+| 0.5070 | 33300 | 7.8697        |
+| 0.5085 | 33400 | 7.8752        |
+| 0.5100 | 33500 | 7.8949        |
+| 0.5116 | 33600 | 7.8844        |
+| 0.5131 | 33700 | 7.8678        |
+| 0.5146 | 33800 | 7.8807        |
+| 0.5161 | 33900 | 7.8904        |
+| 0.5177 | 34000 | 7.8595        |
+| 0.5192 | 34100 | 7.8743        |
+| 0.5207 | 34200 | 7.8716        |
+| 0.5222 | 34300 | 7.8908        |
+| 0.5237 | 34400 | 7.8586        |
+| 0.5253 | 34500 | 7.8698        |
+| 0.5268 | 34600 | 7.871         |
+| 0.5283 | 34700 | 7.8758        |
+| 0.5298 | 34800 | 7.8698        |
+| 0.5314 | 34900 | 7.8578        |
+| 0.5329 | 35000 | 7.8447        |
+| 0.5344 | 35100 | 7.8611        |
+| 0.5359 | 35200 | 7.8727        |
+| 0.5374 | 35300 | 7.8655        |
+| 0.5390 | 35400 | 7.8786        |
+| 0.5405 | 35500 | 7.8706        |
+| 0.5420 | 35600 | 7.8736        |
+| 0.5435 | 35700 | 7.8741        |
+| 0.5451 | 35800 | 7.8801        |
+| 0.5466 | 35900 | 7.8552        |
+| 0.5481 | 36000 | 7.891         |
+| 0.5496 | 36100 | 7.8654        |
+| 0.5511 | 36200 | 7.8689        |
+| 0.5527 | 36300 | 7.869         |
+| 0.5542 | 36400 | 7.8677        |
+| 0.5557 | 36500 | 7.8475        |
+| 0.5572 | 36600 | 7.8691        |
+| 0.5588 | 36700 | 7.8662        |
+| 0.5603 | 36800 | 7.8852        |
+| 0.5618 | 36900 | 7.8632        |
+| 0.5633 | 37000 | 7.8513        |
+| 0.5649 | 37100 | 7.8691        |
+| 0.5664 | 37200 | 7.8513        |
+| 0.5679 | 37300 | 7.8642        |
+| 0.5694 | 37400 | 7.8767        |
+| 0.5709 | 37500 | 7.8693        |
+| 0.5725 | 37600 | 7.8807        |
+| 0.5740 | 37700 | 7.8741        |
+| 0.5755 | 37800 | 7.8708        |
+| 0.5770 | 37900 | 7.8696        |
+| 0.5786 | 38000 | 7.8642        |
+| 0.5801 | 38100 | 7.8688        |
+| 0.5816 | 38200 | 7.8445        |
+| 0.5831 | 38300 | 7.8474        |
+| 0.5846 | 38400 | 7.8608        |
+| 0.5862 | 38500 | 7.846         |
+| 0.5877 | 38600 | 7.8701        |
+| 0.5892 | 38700 | 7.8543        |
+| 0.5907 | 38800 | 7.8704        |
+| 0.5923 | 38900 | 7.8611        |
+| 0.5938 | 39000 | 7.8677        |
+| 0.5953 | 39100 | 7.8625        |
+| 0.5968 | 39200 | 7.8809        |
+| 0.5983 | 39300 | 7.8587        |
+| 0.5999 | 39400 | 7.8566        |
+| 0.6014 | 39500 | 7.8658        |
+| 0.6029 | 39600 | 7.8513        |
+| 0.6044 | 39700 | 7.8685        |
+| 0.6060 | 39800 | 7.8476        |
+| 0.6075 | 39900 | 7.8375        |
+| 0.6090 | 40000 | 7.8707        |
+| 0.6105 | 40100 | 7.8599        |
+| 0.6120 | 40200 | 7.8602        |
+| 0.6136 | 40300 | 7.8509        |
+| 0.6151 | 40400 | 7.8491        |
+| 0.6166 | 40500 | 7.841         |
+| 0.6181 | 40600 | 7.8454        |
+| 0.6197 | 40700 | 7.8492        |
+| 0.6212 | 40800 | 7.8725        |
+| 0.6227 | 40900 | 7.8411        |
+| 0.6242 | 41000 | 7.8496        |
+| 0.6258 | 41100 | 7.8304        |
+| 0.6273 | 41200 | 7.8273        |
+| 0.6288 | 41300 | 7.862         |
+| 0.6303 | 41400 | 7.854         |
+| 0.6318 | 41500 | 7.8462        |
+| 0.6334 | 41600 | 7.8418        |
+| 0.6349 | 41700 | 7.8423        |
+| 0.6364 | 41800 | 7.8522        |
+| 0.6379 | 41900 | 7.8574        |
+| 0.6395 | 42000 | 7.8348        |
+| 0.6410 | 42100 | 7.8371        |
+| 0.6425 | 42200 | 7.8462        |
+| 0.6440 | 42300 | 7.8367        |
+| 0.6455 | 42400 | 7.8649        |
+| 0.6471 | 42500 | 7.8708        |
+| 0.6486 | 42600 | 7.834         |
+| 0.6501 | 42700 | 7.8318        |
+| 0.6516 | 42800 | 7.8604        |
+| 0.6532 | 42900 | 7.8496        |
+| 0.6547 | 43000 | 7.827         |
+| 0.6562 | 43100 | 7.8456        |
+| 0.6577 | 43200 | 7.849         |
+| 0.6592 | 43300 | 7.8772        |
+| 0.6608 | 43400 | 7.8538        |
+| 0.6623 | 43500 | 7.8617        |
+| 0.6638 | 43600 | 7.8309        |
+| 0.6653 | 43700 | 7.8405        |
+| 0.6669 | 43800 | 7.8367        |
+| 0.6684 | 43900 | 7.8552        |
+| 0.6699 | 44000 | 7.8456        |
+| 0.6714 | 44100 | 7.8434        |
+| 0.6729 | 44200 | 7.8215        |
+| 0.6745 | 44300 | 7.8504        |
+| 0.6760 | 44400 | 7.8153        |
+| 0.6775 | 44500 | 7.8521        |
+| 0.6790 | 44600 | 7.8265        |
+| 0.6806 | 44700 | 7.8568        |
+| 0.6821 | 44800 | 7.8373        |
+| 0.6836 | 44900 | 7.8438        |
+| 0.6851 | 45000 | 7.8583        |
+| 0.6867 | 45100 | 7.847         |
+| 0.6882 | 45200 | 7.8383        |
+| 0.6897 | 45300 | 7.838         |
+| 0.6912 | 45400 | 7.8262        |
+| 0.6927 | 45500 | 7.832         |
+| 0.6943 | 45600 | 7.8331        |
+| 0.6958 | 45700 | 7.8472        |
+| 0.6973 | 45800 | 7.838         |
+| 0.6988 | 45900 | 7.8563        |
+| 0.7004 | 46000 | 7.8461        |
+| 0.7019 | 46100 | 7.8381        |
+| 0.7034 | 46200 | 7.8566        |
+| 0.7049 | 46300 | 7.8464        |
+| 0.7064 | 46400 | 7.837         |
+| 0.7080 | 46500 | 7.8358        |
+| 0.7095 | 46600 | 7.8627        |
+| 0.7110 | 46700 | 7.8511        |
+| 0.7125 | 46800 | 7.8442        |
+| 0.7141 | 46900 | 7.8467        |
+| 0.7156 | 47000 | 7.8388        |
+| 0.7171 | 47100 | 7.8385        |
+| 0.7186 | 47200 | 7.851         |
+| 0.7201 | 47300 | 7.8466        |
+| 0.7217 | 47400 | 7.8353        |
+| 0.7232 | 47500 | 7.8536        |
+| 0.7247 | 47600 | 7.8335        |
+| 0.7262 | 47700 | 7.8312        |
+| 0.7278 | 47800 | 7.8338        |
+| 0.7293 | 47900 | 7.8291        |
+| 0.7308 | 48000 | 7.8339        |
+| 0.7323 | 48100 | 7.8368        |
+| 0.7338 | 48200 | 7.8303        |
+| 0.7354 | 48300 | 7.8457        |
+| 0.7369 | 48400 | 7.8312        |
+| 0.7384 | 48500 | 7.8442        |
+| 0.7399 | 48600 | 7.8415        |
+| 0.7415 | 48700 | 7.8326        |
+| 0.7430 | 48800 | 7.83          |
+| 0.7445 | 48900 | 7.8398        |
+| 0.7460 | 49000 | 7.8305        |
+| 0.7476 | 49100 | 7.835         |
+| 0.7491 | 49200 | 7.8421        |
+| 0.7506 | 49300 | 7.8342        |
+| 0.7521 | 49400 | 7.846         |
+| 0.7536 | 49500 | 7.8187        |
+| 0.7552 | 49600 | 7.8314        |
+| 0.7567 | 49700 | 7.8289        |
+| 0.7582 | 49800 | 7.8421        |
+| 0.7597 | 49900 | 7.8559        |
+| 0.7613 | 50000 | 7.8445        |
+| 0.7628 | 50100 | 7.8115        |
+| 0.7643 | 50200 | 7.8541        |
+| 0.7658 | 50300 | 7.8359        |
+| 0.7673 | 50400 | 7.8239        |
+| 0.7689 | 50500 | 7.834         |
+| 0.7704 | 50600 | 7.8407        |
+| 0.7719 | 50700 | 7.8369        |
+| 0.7734 | 50800 | 7.8588        |
+| 0.7750 | 50900 | 7.8307        |
+| 0.7765 | 51000 | 7.8117        |
+| 0.7780 | 51100 | 7.8262        |
+| 0.7795 | 51200 | 7.8324        |
+| 0.7810 | 51300 | 7.8271        |
+| 0.7826 | 51400 | 7.8221        |
+| 0.7841 | 51500 | 7.828         |
+| 0.7856 | 51600 | 7.8217        |
+| 0.7871 | 51700 | 7.8404        |
+| 0.7887 | 51800 | 7.8268        |
+| 0.7902 | 51900 | 7.8256        |
+| 0.7917 | 52000 | 7.8258        |
+| 0.7932 | 52100 | 7.8308        |
+| 0.7948 | 52200 | 7.8252        |
+| 0.7963 | 52300 | 7.8276        |
+| 0.7978 | 52400 | 7.8101        |
+| 0.7993 | 52500 | 7.8217        |
+| 0.8008 | 52600 | 7.8282        |
+| 0.8024 | 52700 | 7.8613        |
+| 0.8039 | 52800 | 7.8352        |
+| 0.8054 | 52900 | 7.8258        |
+| 0.8069 | 53000 | 7.8166        |
+| 0.8085 | 53100 | 7.8245        |
+| 0.8100 | 53200 | 7.8186        |
+| 0.8115 | 53300 | 7.8166        |
+| 0.8130 | 53400 | 7.816         |
+| 0.8145 | 53500 | 7.8353        |
+| 0.8161 | 53600 | 7.8194        |
+| 0.8176 | 53700 | 7.8204        |
+| 0.8191 | 53800 | 7.8354        |
+| 0.8206 | 53900 | 7.8261        |
+| 0.8222 | 54000 | 7.8463        |
+| 0.8237 | 54100 | 7.8254        |
+| 0.8252 | 54200 | 7.818         |
+| 0.8267 | 54300 | 7.8266        |
+| 0.8282 | 54400 | 7.8093        |
+| 0.8298 | 54500 | 7.8297        |
+| 0.8313 | 54600 | 7.8457        |
+| 0.8328 | 54700 | 7.8311        |
+| 0.8343 | 54800 | 7.8281        |
+| 0.8359 | 54900 | 7.8158        |
+| 0.8374 | 55000 | 7.8196        |
+| 0.8389 | 55100 | 7.843         |
+| 0.8404 | 55200 | 7.8168        |
+| 0.8419 | 55300 | 7.8125        |
+| 0.8435 | 55400 | 7.8077        |
+| 0.8450 | 55500 | 7.8112        |
+| 0.8465 | 55600 | 7.8165        |
+| 0.8480 | 55700 | 7.8116        |
+| 0.8496 | 55800 | 7.7858        |
+| 0.8511 | 55900 | 7.8216        |
+| 0.8526 | 56000 | 7.8326        |
+| 0.8541 | 56100 | 7.8207        |
+| 0.8557 | 56200 | 7.8246        |
+| 0.8572 | 56300 | 7.8004        |
+| 0.8587 | 56400 | 7.8195        |
+| 0.8602 | 56500 | 7.8212        |
+| 0.8617 | 56600 | 7.8016        |
+| 0.8633 | 56700 | 7.8264        |
+| 0.8648 | 56800 | 7.7963        |
+| 0.8663 | 56900 | 7.801         |
+| 0.8678 | 57000 | 7.8315        |
+| 0.8694 | 57100 | 7.8263        |
+| 0.8709 | 57200 | 7.7979        |
+| 0.8724 | 57300 | 7.8204        |
+| 0.8739 | 57400 | 7.8276        |
+| 0.8754 | 57500 | 7.8276        |
+| 0.8770 | 57600 | 7.7986        |
+| 0.8785 | 57700 | 7.795         |
+| 0.8800 | 57800 | 7.8201        |
+| 0.8815 | 57900 | 7.8109        |
+| 0.8831 | 58000 | 7.8204        |
+| 0.8846 | 58100 | 7.8294        |
+| 0.8861 | 58200 | 7.8154        |
+| 0.8876 | 58300 | 7.7995        |
+| 0.8891 | 58400 | 7.8059        |
+| 0.8907 | 58500 | 7.826         |
+| 0.8922 | 58600 | 7.8412        |
+| 0.8937 | 58700 | 7.8326        |
+| 0.8952 | 58800 | 7.8078        |
+| 0.8968 | 58900 | 7.8314        |
+| 0.8983 | 59000 | 7.7992        |
+| 0.8998 | 59100 | 7.8209        |
+| 0.9013 | 59200 | 7.8388        |
+| 0.9028 | 59300 | 7.7981        |
+| 0.9044 | 59400 | 7.8047        |
+| 0.9059 | 59500 | 7.8209        |
+| 0.9074 | 59600 | 7.8173        |
+| 0.9089 | 59700 | 7.8073        |
+| 0.9105 | 59800 | 7.8105        |
+| 0.9120 | 59900 | 7.8047        |
+| 0.9135 | 60000 | 7.8104        |
+| 0.9150 | 60100 | 7.8217        |
+| 0.9166 | 60200 | 7.8212        |
+| 0.9181 | 60300 | 7.7984        |
+| 0.9196 | 60400 | 7.8127        |
+| 0.9211 | 60500 | 7.8227        |
+| 0.9226 | 60600 | 7.8057        |
+| 0.9242 | 60700 | 7.8092        |
+| 0.9257 | 60800 | 7.8461        |
+| 0.9272 | 60900 | 7.8068        |
+| 0.9287 | 61000 | 7.8208        |
+| 0.9303 | 61100 | 7.8149        |
+| 0.9318 | 61200 | 7.8076        |
+| 0.9333 | 61300 | 7.8143        |
+| 0.9348 | 61400 | 7.8119        |
+| 0.9363 | 61500 | 7.7992        |
+| 0.9379 | 61600 | 7.8107        |
+| 0.9394 | 61700 | 7.8068        |
+| 0.9409 | 61800 | 7.8168        |
+| 0.9424 | 61900 | 7.8278        |
+| 0.9440 | 62000 | 7.8177        |
+| 0.9455 | 62100 | 7.8153        |
+| 0.9470 | 62200 | 7.8252        |
+| 0.9485 | 62300 | 7.8217        |
+| 0.9500 | 62400 | 7.8236        |
+| 0.9516 | 62500 | 7.8171        |
+| 0.9531 | 62600 | 7.8158        |
+| 0.9546 | 62700 | 7.8186        |
+| 0.9561 | 62800 | 7.834         |
+| 0.9577 | 62900 | 7.788         |
+| 0.9592 | 63000 | 7.7916        |
+| 0.9607 | 63100 | 7.8135        |
+| 0.9622 | 63200 | 7.814         |
+| 0.9637 | 63300 | 7.8263        |
+| 0.9653 | 63400 | 7.8155        |
+| 0.9668 | 63500 | 7.8185        |
+| 0.9683 | 63600 | 7.8054        |
+| 0.9698 | 63700 | 7.8109        |
+| 0.9714 | 63800 | 7.8055        |
+| 0.9729 | 63900 | 7.8135        |
+| 0.9744 | 64000 | 7.8247        |
+| 0.9759 | 64100 | 7.8158        |
+| 0.9775 | 64200 | 7.8103        |
+| 0.9790 | 64300 | 7.8152        |
+| 0.9805 | 64400 | 7.8255        |
+| 0.9820 | 64500 | 7.8087        |
+| 0.9835 | 64600 | 7.8028        |
+| 0.9851 | 64700 | 7.7851        |
+| 0.9866 | 64800 | 7.8105        |
+| 0.9881 | 64900 | 7.8106        |
+| 0.9896 | 65000 | 7.8212        |
+| 0.9912 | 65100 | 7.8047        |
+| 0.9927 | 65200 | 7.8143        |
+| 0.9942 | 65300 | 7.8055        |
+| 0.9957 | 65400 | 7.8047        |
+| 0.9972 | 65500 | 7.8096        |
+| 0.9988 | 65600 | 7.8203        |
+
+</details>
+
+### Framework Versions
+- Python: 3.12.3
+- Sentence Transformers: 5.1.0
+- Transformers: 4.55.4
+- PyTorch: 2.5.1+cu121
+- Accelerate: 1.10.1
+- Datasets: 4.0.0
+- 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",
+}
+```
+
+#### CoSENTLoss
+```bibtex
+@online{kexuefm-8847,
+    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
+    author={Su Jianlin},
+    year={2022},
+    month={Jan},
+    url={https://kexue.fm/archives/8847},
+}
+```
+
+<!--
+## 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 +1,25 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.55.4",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "sentence_transformers": "5.1.0",
+    "transformers": "4.55.4",
+    "pytorch": "2.5.1+cu121"
+  },
+  "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 sha256:608d7a4b39eeb46a9fb20cb672360cfa8375ec47ef492d615e9eb9a681564c57
+size 90864192

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 256,
+    "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 +1,65 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 256,
+  "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]"
+}