Upload folder using huggingface_hub

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

@@ -0,0 +1,1051 @@
+---
+language:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:9172937
+- loss:CoSENTLoss
+base_model: sentence-transformers/all-MiniLM-L6-v2
+widget:
+- source_sentence: unpaid therapist with a built in lie detector mug
+  sentences:
+  - dream and goal sticky notes
+  - mini croissant
+  - frozen peanut butter cookies
+- source_sentence: and
+  sentences:
+  - emmental cheese soup
+  - ball with good grip
+  - swimming remote control car
+- source_sentence: handle mug
+  sentences:
+  - oversized fit tshirt
+  - valentine brownie
+  - aloe eva hair oil replacement
+- source_sentence: healthy food
+  sentences:
+  - beef brisket salad
+  - hdrawn swim shorts
+  - nox balls
+- source_sentence: deluxe mug for morning coffee
+  sentences:
+  - comfort pants
+  - cheddar cheese burrito
+  - polyfibre scarf
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# all-MiniLM-L6-v8-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). 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 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+- **Language:** en
+- **License:** apache-2.0
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'deluxe mug for morning coffee',
+    'polyfibre scarf',
+    'cheddar cheese burrito',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training 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`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `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
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step  | Training Loss |
+|:------:|:-----:|:-------------:|
+| 0.0014 | 100   | 11.4553       |
+| 0.0028 | 200   | 11.5147       |
+| 0.0042 | 300   | 11.1304       |
+| 0.0056 | 400   | 10.7925       |
+| 0.0070 | 500   | 10.4493       |
+| 0.0084 | 600   | 10.2631       |
+| 0.0098 | 700   | 9.987         |
+| 0.0112 | 800   | 9.8477        |
+| 0.0126 | 900   | 9.6295        |
+| 0.0140 | 1000  | 9.3638        |
+| 0.0153 | 1100  | 9.1913        |
+| 0.0167 | 1200  | 8.9688        |
+| 0.0181 | 1300  | 8.808         |
+| 0.0195 | 1400  | 8.6993        |
+| 0.0209 | 1500  | 8.6078        |
+| 0.0223 | 1600  | 8.5739        |
+| 0.0237 | 1700  | 8.5575        |
+| 0.0251 | 1800  | 8.5173        |
+| 0.0265 | 1900  | 8.4983        |
+| 0.0279 | 2000  | 8.4662        |
+| 0.0293 | 2100  | 8.4408        |
+| 0.0307 | 2200  | 8.4136        |
+| 0.0321 | 2300  | 8.4002        |
+| 0.0335 | 2400  | 8.3883        |
+| 0.0349 | 2500  | 8.3785        |
+| 0.0363 | 2600  | 8.3458        |
+| 0.0377 | 2700  | 8.3617        |
+| 0.0391 | 2800  | 8.3338        |
+| 0.0405 | 2900  | 8.3281        |
+| 0.0419 | 3000  | 8.3043        |
+| 0.0433 | 3100  | 8.3087        |
+| 0.0447 | 3200  | 8.2913        |
+| 0.0460 | 3300  | 8.2854        |
+| 0.0474 | 3400  | 8.2408        |
+| 0.0488 | 3500  | 8.2628        |
+| 0.0502 | 3600  | 8.2401        |
+| 0.0516 | 3700  | 8.2538        |
+| 0.0530 | 3800  | 8.2103        |
+| 0.0544 | 3900  | 8.2221        |
+| 0.0558 | 4000  | 8.2248        |
+| 0.0572 | 4100  | 8.2045        |
+| 0.0586 | 4200  | 8.2008        |
+| 0.0600 | 4300  | 8.196         |
+| 0.0614 | 4400  | 8.1757        |
+| 0.0628 | 4500  | 8.1845        |
+| 0.0642 | 4600  | 8.1714        |
+| 0.0656 | 4700  | 8.1745        |
+| 0.0670 | 4800  | 8.1702        |
+| 0.0684 | 4900  | 8.1767        |
+| 0.0698 | 5000  | 8.1379        |
+| 0.0712 | 5100  | 8.1473        |
+| 0.0726 | 5200  | 8.1443        |
+| 0.0740 | 5300  | 8.1173        |
+| 0.0754 | 5400  | 8.121         |
+| 0.0767 | 5500  | 8.136         |
+| 0.0781 | 5600  | 8.1246        |
+| 0.0795 | 5700  | 8.0983        |
+| 0.0809 | 5800  | 8.1023        |
+| 0.0823 | 5900  | 8.1013        |
+| 0.0837 | 6000  | 8.0657        |
+| 0.0851 | 6100  | 8.0998        |
+| 0.0865 | 6200  | 8.0585        |
+| 0.0879 | 6300  | 8.1082        |
+| 0.0893 | 6400  | 8.0652        |
+| 0.0907 | 6500  | 8.0808        |
+| 0.0921 | 6600  | 8.0756        |
+| 0.0935 | 6700  | 8.0279        |
+| 0.0949 | 6800  | 8.0659        |
+| 0.0963 | 6900  | 8.0428        |
+| 0.0977 | 7000  | 8.0363        |
+| 0.0991 | 7100  | 8.0343        |
+| 0.1005 | 7200  | 8.0488        |
+| 0.1019 | 7300  | 8.0225        |
+| 0.1033 | 7400  | 8.0203        |
+| 0.1047 | 7500  | 8.0248        |
+| 0.1061 | 7600  | 7.9882        |
+| 0.1074 | 7700  | 7.9956        |
+| 0.1088 | 7800  | 8.0338        |
+| 0.1102 | 7900  | 7.9827        |
+| 0.1116 | 8000  | 7.9849        |
+| 0.1130 | 8100  | 8.0072        |
+| 0.1144 | 8200  | 7.9708        |
+| 0.1158 | 8300  | 7.9786        |
+| 0.1172 | 8400  | 7.9983        |
+| 0.1186 | 8500  | 7.9762        |
+| 0.1200 | 8600  | 7.9955        |
+| 0.1214 | 8700  | 7.9969        |
+| 0.1228 | 8800  | 7.9913        |
+| 0.1242 | 8900  | 7.9512        |
+| 0.1256 | 9000  | 7.9672        |
+| 0.1270 | 9100  | 7.9853        |
+| 0.1284 | 9200  | 7.9626        |
+| 0.1298 | 9300  | 7.9767        |
+| 0.1312 | 9400  | 7.9404        |
+| 0.1326 | 9500  | 7.9076        |
+| 0.1340 | 9600  | 7.968         |
+| 0.1354 | 9700  | 7.9432        |
+| 0.1367 | 9800  | 7.9255        |
+| 0.1381 | 9900  | 7.9095        |
+| 0.1395 | 10000 | 7.9337        |
+| 0.1409 | 10100 | 7.9464        |
+| 0.1423 | 10200 | 7.9218        |
+| 0.1437 | 10300 | 7.9102        |
+| 0.1451 | 10400 | 7.9379        |
+| 0.1465 | 10500 | 7.8907        |
+| 0.1479 | 10600 | 7.8968        |
+| 0.1493 | 10700 | 7.9193        |
+| 0.1507 | 10800 | 7.9327        |
+| 0.1521 | 10900 | 7.896         |
+| 0.1535 | 11000 | 7.9228        |
+| 0.1549 | 11100 | 7.9253        |
+| 0.1563 | 11200 | 7.8825        |
+| 0.1577 | 11300 | 7.8812        |
+| 0.1591 | 11400 | 7.8883        |
+| 0.1605 | 11500 | 7.8721        |
+| 0.1619 | 11600 | 7.9218        |
+| 0.1633 | 11700 | 7.8893        |
+| 0.1647 | 11800 | 7.8961        |
+| 0.1661 | 11900 | 7.8647        |
+| 0.1674 | 12000 | 7.89          |
+| 0.1688 | 12100 | 7.8422        |
+| 0.1702 | 12200 | 7.9348        |
+| 0.1716 | 12300 | 7.8808        |
+| 0.1730 | 12400 | 7.8788        |
+| 0.1744 | 12500 | 7.8794        |
+| 0.1758 | 12600 | 7.848         |
+| 0.1772 | 12700 | 7.8279        |
+| 0.1786 | 12800 | 7.8655        |
+| 0.1800 | 12900 | 7.8612        |
+| 0.1814 | 13000 | 7.828         |
+| 0.1828 | 13100 | 7.8419        |
+| 0.1842 | 13200 | 7.8574        |
+| 0.1856 | 13300 | 7.8688        |
+| 0.1870 | 13400 | 7.8408        |
+| 0.1884 | 13500 | 7.8172        |
+| 0.1898 | 13600 | 7.8579        |
+| 0.1912 | 13700 | 7.8392        |
+| 0.1926 | 13800 | 7.849         |
+| 0.1940 | 13900 | 7.8485        |
+| 0.1954 | 14000 | 7.861         |
+| 0.1968 | 14100 | 7.8257        |
+| 0.1981 | 14200 | 7.8647        |
+| 0.1995 | 14300 | 7.857         |
+| 0.2009 | 14400 | 7.8031        |
+| 0.2023 | 14500 | 7.8498        |
+| 0.2037 | 14600 | 7.8175        |
+| 0.2051 | 14700 | 7.8474        |
+| 0.2065 | 14800 | 7.8158        |
+| 0.2079 | 14900 | 7.7777        |
+| 0.2093 | 15000 | 7.8362        |
+| 0.2107 | 15100 | 7.8387        |
+| 0.2121 | 15200 | 7.8225        |
+| 0.2135 | 15300 | 7.8627        |
+| 0.2149 | 15400 | 7.8543        |
+| 0.2163 | 15500 | 7.8096        |
+| 0.2177 | 15600 | 7.8201        |
+| 0.2191 | 15700 | 7.8178        |
+| 0.2205 | 15800 | 7.8138        |
+| 0.2219 | 15900 | 7.8384        |
+| 0.2233 | 16000 | 7.7811        |
+| 0.2247 | 16100 | 7.82          |
+| 0.2261 | 16200 | 7.7731        |
+| 0.2275 | 16300 | 7.8508        |
+| 0.2288 | 16400 | 7.8087        |
+| 0.2302 | 16500 | 7.7959        |
+| 0.2316 | 16600 | 7.7857        |
+| 0.2330 | 16700 | 7.7946        |
+| 0.2344 | 16800 | 7.7884        |
+| 0.2358 | 16900 | 7.8226        |
+| 0.2372 | 17000 | 7.7811        |
+| 0.2386 | 17100 | 7.778         |
+| 0.2400 | 17200 | 7.7825        |
+| 0.2414 | 17300 | 7.782         |
+| 0.2428 | 17400 | 7.8164        |
+| 0.2442 | 17500 | 7.7514        |
+| 0.2456 | 17600 | 7.7744        |
+| 0.2470 | 17700 | 7.7974        |
+| 0.2484 | 17800 | 7.7913        |
+| 0.2498 | 17900 | 7.757         |
+| 0.2512 | 18000 | 7.7724        |
+| 0.2526 | 18100 | 7.7772        |
+| 0.2540 | 18200 | 7.7723        |
+| 0.2554 | 18300 | 7.753         |
+| 0.2568 | 18400 | 7.8055        |
+| 0.2581 | 18500 | 7.7878        |
+| 0.2595 | 18600 | 7.7822        |
+| 0.2609 | 18700 | 7.7923        |
+| 0.2623 | 18800 | 7.8378        |
+| 0.2637 | 18900 | 7.8226        |
+| 0.2651 | 19000 | 7.8015        |
+| 0.2665 | 19100 | 7.7355        |
+| 0.2679 | 19200 | 7.789         |
+| 0.2693 | 19300 | 7.7473        |
+| 0.2707 | 19400 | 7.7521        |
+| 0.2721 | 19500 | 7.7867        |
+| 0.2735 | 19600 | 7.7597        |
+| 0.2749 | 19700 | 7.7506        |
+| 0.2763 | 19800 | 7.732         |
+| 0.2777 | 19900 | 7.7288        |
+| 0.2791 | 20000 | 7.7317        |
+| 0.2805 | 20100 | 7.7495        |
+| 0.2819 | 20200 | 7.7236        |
+| 0.2833 | 20300 | 7.7489        |
+| 0.2847 | 20400 | 7.7592        |
+| 0.2861 | 20500 | 7.7455        |
+| 0.2875 | 20600 | 7.7623        |
+| 0.2888 | 20700 | 7.7774        |
+| 0.2902 | 20800 | 7.7485        |
+| 0.2916 | 20900 | 7.7043        |
+| 0.2930 | 21000 | 7.8039        |
+| 0.2944 | 21100 | 7.7383        |
+| 0.2958 | 21200 | 7.759         |
+| 0.2972 | 21300 | 7.7362        |
+| 0.2986 | 21400 | 7.7788        |
+| 0.3000 | 21500 | 7.7244        |
+| 0.3014 | 21600 | 7.72          |
+| 0.3028 | 21700 | 7.7453        |
+| 0.3042 | 21800 | 7.729         |
+| 0.3056 | 21900 | 7.7735        |
+| 0.3070 | 22000 | 7.7185        |
+| 0.3084 | 22100 | 7.7641        |
+| 0.3098 | 22200 | 7.7293        |
+| 0.3112 | 22300 | 7.7401        |
+| 0.3126 | 22400 | 7.725         |
+| 0.3140 | 22500 | 7.7315        |
+| 0.3154 | 22600 | 7.716         |
+| 0.3168 | 22700 | 7.7576        |
+| 0.3182 | 22800 | 7.7088        |
+| 0.3195 | 22900 | 7.7428        |
+| 0.3209 | 23000 | 7.7266        |
+| 0.3223 | 23100 | 7.7246        |
+| 0.3237 | 23200 | 7.7084        |
+| 0.3251 | 23300 | 7.7094        |
+| 0.3265 | 23400 | 7.7081        |
+| 0.3279 | 23500 | 7.7472        |
+| 0.3293 | 23600 | 7.7581        |
+| 0.3307 | 23700 | 7.7264        |
+| 0.3321 | 23800 | 7.7262        |
+| 0.3335 | 23900 | 7.7252        |
+| 0.3349 | 24000 | 7.7219        |
+| 0.3363 | 24100 | 7.706         |
+| 0.3377 | 24200 | 7.7372        |
+| 0.3391 | 24300 | 7.6965        |
+| 0.3405 | 24400 | 7.6865        |
+| 0.3419 | 24500 | 7.6798        |
+| 0.3433 | 24600 | 7.6962        |
+| 0.3447 | 24700 | 7.701         |
+| 0.3461 | 24800 | 7.6722        |
+| 0.3475 | 24900 | 7.7453        |
+| 0.3489 | 25000 | 7.6463        |
+| 0.3502 | 25100 | 7.7256        |
+| 0.3516 | 25200 | 7.693         |
+| 0.3530 | 25300 | 7.7306        |
+| 0.3544 | 25400 | 7.7037        |
+| 0.3558 | 25500 | 7.6733        |
+| 0.3572 | 25600 | 7.7202        |
+| 0.3586 | 25700 | 7.6866        |
+| 0.3600 | 25800 | 7.715         |
+| 0.3614 | 25900 | 7.6925        |
+| 0.3628 | 26000 | 7.6961        |
+| 0.3642 | 26100 | 7.6752        |
+| 0.3656 | 26200 | 7.7377        |
+| 0.3670 | 26300 | 7.6744        |
+| 0.3684 | 26400 | 7.6698        |
+| 0.3698 | 26500 | 7.6931        |
+| 0.3712 | 26600 | 7.6789        |
+| 0.3726 | 26700 | 7.6736        |
+| 0.3740 | 26800 | 7.6918        |
+| 0.3754 | 26900 | 7.7129        |
+| 0.3768 | 27000 | 7.7179        |
+| 0.3782 | 27100 | 7.6747        |
+| 0.3795 | 27200 | 7.6809        |
+| 0.3809 | 27300 | 7.6803        |
+| 0.3823 | 27400 | 7.6777        |
+| 0.3837 | 27500 | 7.6702        |
+| 0.3851 | 27600 | 7.7005        |
+| 0.3865 | 27700 | 7.6671        |
+| 0.3879 | 27800 | 7.6873        |
+| 0.3893 | 27900 | 7.6919        |
+| 0.3907 | 28000 | 7.6987        |
+| 0.3921 | 28100 | 7.6641        |
+| 0.3935 | 28200 | 7.6449        |
+| 0.3949 | 28300 | 7.6715        |
+| 0.3963 | 28400 | 7.6672        |
+| 0.3977 | 28500 | 7.6796        |
+| 0.3991 | 28600 | 7.7085        |
+| 0.4005 | 28700 | 7.6557        |
+| 0.4019 | 28800 | 7.6592        |
+| 0.4033 | 28900 | 7.6695        |
+| 0.4047 | 29000 | 7.6734        |
+| 0.4061 | 29100 | 7.6499        |
+| 0.4075 | 29200 | 7.6472        |
+| 0.4089 | 29300 | 7.6705        |
+| 0.4102 | 29400 | 7.6856        |
+| 0.4116 | 29500 | 7.6474        |
+| 0.4130 | 29600 | 7.6581        |
+| 0.4144 | 29700 | 7.6699        |
+| 0.4158 | 29800 | 7.6693        |
+| 0.4172 | 29900 | 7.6716        |
+| 0.4186 | 30000 | 7.6594        |
+| 0.4200 | 30100 | 7.6391        |
+| 0.4214 | 30200 | 7.6758        |
+| 0.4228 | 30300 | 7.652         |
+| 0.4242 | 30400 | 7.6312        |
+| 0.4256 | 30500 | 7.6538        |
+| 0.4270 | 30600 | 7.6959        |
+| 0.4284 | 30700 | 7.7324        |
+| 0.4298 | 30800 | 7.6529        |
+| 0.4312 | 30900 | 7.6528        |
+| 0.4326 | 31000 | 7.7036        |
+| 0.4340 | 31100 | 7.6794        |
+| 0.4354 | 31200 | 7.6603        |
+| 0.4368 | 31300 | 7.6372        |
+| 0.4382 | 31400 | 7.6427        |
+| 0.4396 | 31500 | 7.6852        |
+| 0.4409 | 31600 | 7.6987        |
+| 0.4423 | 31700 | 7.6385        |
+| 0.4437 | 31800 | 7.701         |
+| 0.4451 | 31900 | 7.6702        |
+| 0.4465 | 32000 | 7.6551        |
+| 0.4479 | 32100 | 7.6464        |
+| 0.4493 | 32200 | 7.667         |
+| 0.4507 | 32300 | 7.628         |
+| 0.4521 | 32400 | 7.7012        |
+| 0.4535 | 32500 | 7.6333        |
+| 0.4549 | 32600 | 7.6707        |
+| 0.4563 | 32700 | 7.6304        |
+| 0.4577 | 32800 | 7.6719        |
+| 0.4591 | 32900 | 7.6744        |
+| 0.4605 | 33000 | 7.7102        |
+| 0.4619 | 33100 | 7.6918        |
+| 0.4633 | 33200 | 7.7018        |
+| 0.4647 | 33300 | 7.6131        |
+| 0.4661 | 33400 | 7.6476        |
+| 0.4675 | 33500 | 7.6594        |
+| 0.4689 | 33600 | 7.6301        |
+| 0.4703 | 33700 | 7.6134        |
+| 0.4716 | 33800 | 7.7383        |
+| 0.4730 | 33900 | 7.6253        |
+| 0.4744 | 34000 | 7.662         |
+| 0.4758 | 34100 | 7.6341        |
+| 0.4772 | 34200 | 7.6622        |
+| 0.4786 | 34300 | 7.6429        |
+| 0.4800 | 34400 | 7.6777        |
+| 0.4814 | 34500 | 7.6089        |
+| 0.4828 | 34600 | 7.6382        |
+| 0.4842 | 34700 | 7.6324        |
+| 0.4856 | 34800 | 7.6176        |
+| 0.4870 | 34900 | 7.624         |
+| 0.4884 | 35000 | 7.6163        |
+| 0.4898 | 35100 | 7.6503        |
+| 0.4912 | 35200 | 7.6609        |
+| 0.4926 | 35300 | 7.6587        |
+| 0.4940 | 35400 | 7.5999        |
+| 0.4954 | 35500 | 7.586         |
+| 0.4968 | 35600 | 7.6585        |
+| 0.4982 | 35700 | 7.7349        |
+| 0.4996 | 35800 | 7.642         |
+| 0.5009 | 35900 | 7.646         |
+| 0.5023 | 36000 | 7.5942        |
+| 0.5037 | 36100 | 7.6477        |
+| 0.5051 | 36200 | 7.6259        |
+| 0.5065 | 36300 | 7.5926        |
+| 0.5079 | 36400 | 7.6166        |
+| 0.5093 | 36500 | 7.6323        |
+| 0.5107 | 36600 | 7.6324        |
+| 0.5121 | 36700 | 7.6411        |
+| 0.5135 | 36800 | 7.6343        |
+| 0.5149 | 36900 | 7.6313        |
+| 0.5163 | 37000 | 7.6187        |
+| 0.5177 | 37100 | 7.6545        |
+| 0.5191 | 37200 | 7.6555        |
+| 0.5205 | 37300 | 7.6984        |
+| 0.5219 | 37400 | 7.6638        |
+| 0.5233 | 37500 | 7.6093        |
+| 0.5247 | 37600 | 7.5925        |
+| 0.5261 | 37700 | 7.6281        |
+| 0.5275 | 37800 | 7.6349        |
+| 0.5289 | 37900 | 7.6152        |
+| 0.5303 | 38000 | 7.6531        |
+| 0.5316 | 38100 | 7.6078        |
+| 0.5330 | 38200 | 7.6775        |
+| 0.5344 | 38300 | 7.6268        |
+| 0.5358 | 38400 | 7.641         |
+| 0.5372 | 38500 | 7.6721        |
+| 0.5386 | 38600 | 7.6069        |
+| 0.5400 | 38700 | 7.6174        |
+| 0.5414 | 38800 | 7.6407        |
+| 0.5428 | 38900 | 7.6226        |
+| 0.5442 | 39000 | 7.5843        |
+| 0.5456 | 39100 | 7.6588        |
+| 0.5470 | 39200 | 7.6405        |
+| 0.5484 | 39300 | 7.5908        |
+| 0.5498 | 39400 | 7.6203        |
+| 0.5512 | 39500 | 7.608         |
+| 0.5526 | 39600 | 7.6177        |
+| 0.5540 | 39700 | 7.606         |
+| 0.5554 | 39800 | 7.7102        |
+| 0.5568 | 39900 | 7.6252        |
+| 0.5582 | 40000 | 7.6235        |
+| 0.5596 | 40100 | 7.6325        |
+| 0.5610 | 40200 | 7.6146        |
+| 0.5623 | 40300 | 7.6386        |
+| 0.5637 | 40400 | 7.6189        |
+| 0.5651 | 40500 | 7.638         |
+| 0.5665 | 40600 | 7.5859        |
+| 0.5679 | 40700 | 7.5737        |
+| 0.5693 | 40800 | 7.6331        |
+| 0.5707 | 40900 | 7.6265        |
+| 0.5721 | 41000 | 7.6475        |
+| 0.5735 | 41100 | 7.5966        |
+| 0.5749 | 41200 | 7.6331        |
+| 0.5763 | 41300 | 7.5655        |
+| 0.5777 | 41400 | 7.6727        |
+| 0.5791 | 41500 | 7.5972        |
+| 0.5805 | 41600 | 7.5911        |
+| 0.5819 | 41700 | 7.6734        |
+| 0.5833 | 41800 | 7.6528        |
+| 0.5847 | 41900 | 7.6063        |
+| 0.5861 | 42000 | 7.6496        |
+| 0.5875 | 42100 | 7.6225        |
+| 0.5889 | 42200 | 7.6863        |
+| 0.5903 | 42300 | 7.6145        |
+| 0.5916 | 42400 | 7.6072        |
+| 0.5930 | 42500 | 7.625         |
+| 0.5944 | 42600 | 7.6087        |
+| 0.5958 | 42700 | 7.6622        |
+| 0.5972 | 42800 | 7.5619        |
+| 0.5986 | 42900 | 7.6563        |
+| 0.6000 | 43000 | 7.5958        |
+| 0.6014 | 43100 | 7.6107        |
+| 0.6028 | 43200 | 7.6208        |
+| 0.6042 | 43300 | 7.5973        |
+| 0.6056 | 43400 | 7.5928        |
+| 0.6070 | 43500 | 7.637         |
+| 0.6084 | 43600 | 7.5659        |
+| 0.6098 | 43700 | 7.5921        |
+| 0.6112 | 43800 | 7.5961        |
+| 0.6126 | 43900 | 7.5614        |
+| 0.6140 | 44000 | 7.6366        |
+| 0.6154 | 44100 | 7.5947        |
+| 0.6168 | 44200 | 7.5976        |
+| 0.6182 | 44300 | 7.6406        |
+| 0.6196 | 44400 | 7.585         |
+| 0.6210 | 44500 | 7.5722        |
+| 0.6223 | 44600 | 7.6193        |
+| 0.6237 | 44700 | 7.6249        |
+| 0.6251 | 44800 | 7.6208        |
+| 0.6265 | 44900 | 7.6293        |
+| 0.6279 | 45000 | 7.6023        |
+| 0.6293 | 45100 | 7.5996        |
+| 0.6307 | 45200 | 7.5553        |
+| 0.6321 | 45300 | 7.5996        |
+| 0.6335 | 45400 | 7.5994        |
+| 0.6349 | 45500 | 7.6691        |
+| 0.6363 | 45600 | 7.6051        |
+| 0.6377 | 45700 | 7.6589        |
+| 0.6391 | 45800 | 7.6217        |
+| 0.6405 | 45900 | 7.6053        |
+| 0.6419 | 46000 | 7.6082        |
+| 0.6433 | 46100 | 7.5913        |
+| 0.6447 | 46200 | 7.5742        |
+| 0.6461 | 46300 | 7.597         |
+| 0.6475 | 46400 | 7.5759        |
+| 0.6489 | 46500 | 7.5964        |
+| 0.6503 | 46600 | 7.6719        |
+| 0.6517 | 46700 | 7.605         |
+| 0.6530 | 46800 | 7.5705        |
+| 0.6544 | 46900 | 7.6292        |
+| 0.6558 | 47000 | 7.5978        |
+| 0.6572 | 47100 | 7.5525        |
+| 0.6586 | 47200 | 7.5838        |
+| 0.6600 | 47300 | 7.5672        |
+| 0.6614 | 47400 | 7.6041        |
+| 0.6628 | 47500 | 7.6255        |
+| 0.6642 | 47600 | 7.5415        |
+| 0.6656 | 47700 | 7.61          |
+| 0.6670 | 47800 | 7.573         |
+| 0.6684 | 47900 | 7.6413        |
+| 0.6698 | 48000 | 7.6277        |
+| 0.6712 | 48100 | 7.5903        |
+| 0.6726 | 48200 | 7.6542        |
+| 0.6740 | 48300 | 7.5772        |
+| 0.6754 | 48400 | 7.5991        |
+| 0.6768 | 48500 | 7.5853        |
+| 0.6782 | 48600 | 7.5909        |
+| 0.6796 | 48700 | 7.5912        |
+| 0.6810 | 48800 | 7.6052        |
+| 0.6824 | 48900 | 7.632         |
+| 0.6837 | 49000 | 7.5851        |
+| 0.6851 | 49100 | 7.6688        |
+| 0.6865 | 49200 | 7.6091        |
+| 0.6879 | 49300 | 7.5745        |
+| 0.6893 | 49400 | 7.5833        |
+| 0.6907 | 49500 | 7.5777        |
+| 0.6921 | 49600 | 7.5637        |
+| 0.6935 | 49700 | 7.5622        |
+| 0.6949 | 49800 | 7.5633        |
+| 0.6963 | 49900 | 7.6023        |
+| 0.6977 | 50000 | 7.6103        |
+| 0.6991 | 50100 | 7.547         |
+| 0.7005 | 50200 | 7.5907        |
+| 0.7019 | 50300 | 7.5882        |
+| 0.7033 | 50400 | 7.5875        |
+| 0.7047 | 50500 | 7.5909        |
+| 0.7061 | 50600 | 7.6021        |
+| 0.7075 | 50700 | 7.549         |
+| 0.7089 | 50800 | 7.6511        |
+| 0.7103 | 50900 | 7.6606        |
+| 0.7117 | 51000 | 7.5967        |
+| 0.7130 | 51100 | 7.5722        |
+| 0.7144 | 51200 | 7.6129        |
+| 0.7158 | 51300 | 7.5736        |
+| 0.7172 | 51400 | 7.5799        |
+| 0.7186 | 51500 | 7.6209        |
+| 0.7200 | 51600 | 7.595         |
+| 0.7214 | 51700 | 7.5484        |
+| 0.7228 | 51800 | 7.5999        |
+| 0.7242 | 51900 | 7.5638        |
+| 0.7256 | 52000 | 7.5654        |
+| 0.7270 | 52100 | 7.6303        |
+| 0.7284 | 52200 | 7.5485        |
+| 0.7298 | 52300 | 7.676         |
+| 0.7312 | 52400 | 7.6376        |
+| 0.7326 | 52500 | 7.557         |
+| 0.7340 | 52600 | 7.5631        |
+| 0.7354 | 52700 | 7.6637        |
+| 0.7368 | 52800 | 7.588         |
+| 0.7382 | 52900 | 7.5771        |
+| 0.7396 | 53000 | 7.5766        |
+| 0.7410 | 53100 | 7.5731        |
+| 0.7424 | 53200 | 7.508         |
+| 0.7437 | 53300 | 7.6023        |
+| 0.7451 | 53400 | 7.5796        |
+| 0.7465 | 53500 | 7.5593        |
+| 0.7479 | 53600 | 7.5516        |
+| 0.7493 | 53700 | 7.5973        |
+| 0.7507 | 53800 | 7.5868        |
+| 0.7521 | 53900 | 7.623         |
+| 0.7535 | 54000 | 7.5972        |
+| 0.7549 | 54100 | 7.6304        |
+| 0.7563 | 54200 | 7.5927        |
+| 0.7577 | 54300 | 7.5351        |
+| 0.7591 | 54400 | 7.5732        |
+| 0.7605 | 54500 | 7.6676        |
+| 0.7619 | 54600 | 7.6103        |
+| 0.7633 | 54700 | 7.5572        |
+| 0.7647 | 54800 | 7.574         |
+| 0.7661 | 54900 | 7.555         |
+| 0.7675 | 55000 | 7.6347        |
+| 0.7689 | 55100 | 7.5827        |
+| 0.7703 | 55200 | 7.678         |
+| 0.7717 | 55300 | 7.5577        |
+| 0.7731 | 55400 | 7.5606        |
+| 0.7744 | 55500 | 7.5284        |
+| 0.7758 | 55600 | 7.5561        |
+| 0.7772 | 55700 | 7.6569        |
+| 0.7786 | 55800 | 7.5604        |
+| 0.7800 | 55900 | 7.6444        |
+| 0.7814 | 56000 | 7.602         |
+| 0.7828 | 56100 | 7.5532        |
+| 0.7842 | 56200 | 7.5524        |
+| 0.7856 | 56300 | 7.654         |
+| 0.7870 | 56400 | 7.5799        |
+| 0.7884 | 56500 | 7.5609        |
+| 0.7898 | 56600 | 7.5625        |
+| 0.7912 | 56700 | 7.571         |
+| 0.7926 | 56800 | 7.5126        |
+| 0.7940 | 56900 | 7.5644        |
+| 0.7954 | 57000 | 7.5508        |
+| 0.7968 | 57100 | 7.5183        |
+| 0.7982 | 57200 | 7.5749        |
+| 0.7996 | 57300 | 7.5339        |
+| 0.8010 | 57400 | 7.5739        |
+| 0.8024 | 57500 | 7.5492        |
+| 0.8038 | 57600 | 7.5781        |
+| 0.8051 | 57700 | 7.5753        |
+| 0.8065 | 57800 | 7.5485        |
+| 0.8079 | 57900 | 7.5608        |
+| 0.8093 | 58000 | 7.5515        |
+| 0.8107 | 58100 | 7.6011        |
+| 0.8121 | 58200 | 7.6072        |
+| 0.8135 | 58300 | 7.5615        |
+| 0.8149 | 58400 | 7.5583        |
+| 0.8163 | 58500 | 7.5423        |
+| 0.8177 | 58600 | 7.5852        |
+| 0.8191 | 58700 | 7.5612        |
+| 0.8205 | 58800 | 7.5808        |
+| 0.8219 | 58900 | 7.5888        |
+| 0.8233 | 59000 | 7.6449        |
+| 0.8247 | 59100 | 7.6599        |
+| 0.8261 | 59200 | 7.573         |
+| 0.8275 | 59300 | 7.5533        |
+| 0.8289 | 59400 | 7.5423        |
+| 0.8303 | 59500 | 7.5879        |
+| 0.8317 | 59600 | 7.5699        |
+| 0.8331 | 59700 | 7.5792        |
+| 0.8344 | 59800 | 7.5552        |
+| 0.8358 | 59900 | 7.5982        |
+| 0.8372 | 60000 | 7.5984        |
+| 0.8386 | 60100 | 7.5383        |
+| 0.8400 | 60200 | 7.5518        |
+| 0.8414 | 60300 | 7.5587        |
+| 0.8428 | 60400 | 7.5152        |
+| 0.8442 | 60500 | 7.5945        |
+| 0.8456 | 60600 | 7.5674        |
+| 0.8470 | 60700 | 7.5527        |
+| 0.8484 | 60800 | 7.5941        |
+| 0.8498 | 60900 | 7.5964        |
+| 0.8512 | 61000 | 7.5625        |
+| 0.8526 | 61100 | 7.5526        |
+| 0.8540 | 61200 | 7.5592        |
+| 0.8554 | 61300 | 7.5593        |
+| 0.8568 | 61400 | 7.5392        |
+| 0.8582 | 61500 | 7.641         |
+| 0.8596 | 61600 | 7.6258        |
+| 0.8610 | 61700 | 7.6588        |
+| 0.8624 | 61800 | 7.5707        |
+| 0.8638 | 61900 | 7.5171        |
+| 0.8651 | 62000 | 7.6107        |
+| 0.8665 | 62100 | 7.6272        |
+| 0.8679 | 62200 | 7.5549        |
+| 0.8693 | 62300 | 7.5535        |
+| 0.8707 | 62400 | 7.6454        |
+| 0.8721 | 62500 | 7.5498        |
+| 0.8735 | 62600 | 7.5898        |
+| 0.8749 | 62700 | 7.5461        |
+| 0.8763 | 62800 | 7.5611        |
+| 0.8777 | 62900 | 7.6068        |
+| 0.8791 | 63000 | 7.6001        |
+| 0.8805 | 63100 | 7.5407        |
+| 0.8819 | 63200 | 7.5961        |
+| 0.8833 | 63300 | 7.5839        |
+| 0.8847 | 63400 | 7.5426        |
+| 0.8861 | 63500 | 7.6011        |
+| 0.8875 | 63600 | 7.5708        |
+| 0.8889 | 63700 | 7.5964        |
+| 0.8903 | 63800 | 7.5704        |
+| 0.8917 | 63900 | 7.5372        |
+| 0.8931 | 64000 | 7.5835        |
+| 0.8945 | 64100 | 7.5483        |
+| 0.8958 | 64200 | 7.544         |
+| 0.8972 | 64300 | 7.5677        |
+| 0.8986 | 64400 | 7.5636        |
+| 0.9000 | 64500 | 7.5914        |
+| 0.9014 | 64600 | 7.5789        |
+| 0.9028 | 64700 | 7.5666        |
+| 0.9042 | 64800 | 7.5866        |
+| 0.9056 | 64900 | 7.6195        |
+| 0.9070 | 65000 | 7.5388        |
+| 0.9084 | 65100 | 7.5821        |
+| 0.9098 | 65200 | 7.6767        |
+| 0.9112 | 65300 | 7.6625        |
+| 0.9126 | 65400 | 7.5812        |
+| 0.9140 | 65500 | 7.5026        |
+| 0.9154 | 65600 | 7.5524        |
+| 0.9168 | 65700 | 7.5851        |
+| 0.9182 | 65800 | 7.5762        |
+| 0.9196 | 65900 | 7.5466        |
+| 0.9210 | 66000 | 7.6039        |
+| 0.9224 | 66100 | 7.6041        |
+| 0.9238 | 66200 | 7.5805        |
+| 0.9252 | 66300 | 7.6334        |
+| 0.9265 | 66400 | 7.5348        |
+| 0.9279 | 66500 | 7.6065        |
+| 0.9293 | 66600 | 7.5003        |
+| 0.9307 | 66700 | 7.5512        |
+| 0.9321 | 66800 | 7.5404        |
+| 0.9335 | 66900 | 7.6176        |
+| 0.9349 | 67000 | 7.5634        |
+| 0.9363 | 67100 | 7.5786        |
+| 0.9377 | 67200 | 7.6327        |
+| 0.9391 | 67300 | 7.5532        |
+| 0.9405 | 67400 | 7.5362        |
+| 0.9419 | 67500 | 7.5844        |
+| 0.9433 | 67600 | 7.5632        |
+| 0.9447 | 67700 | 7.553         |
+| 0.9461 | 67800 | 7.5422        |
+| 0.9475 | 67900 | 7.5483        |
+| 0.9489 | 68000 | 7.5477        |
+| 0.9503 | 68100 | 7.5423        |
+| 0.9517 | 68200 | 7.5656        |
+| 0.9531 | 68300 | 7.5573        |
+| 0.9545 | 68400 | 7.525         |
+| 0.9558 | 68500 | 7.55          |
+| 0.9572 | 68600 | 7.5341        |
+| 0.9586 | 68700 | 7.5318        |
+| 0.9600 | 68800 | 7.5691        |
+| 0.9614 | 68900 | 7.5793        |
+| 0.9628 | 69000 | 7.5615        |
+| 0.9642 | 69100 | 7.5348        |
+| 0.9656 | 69200 | 7.5384        |
+| 0.9670 | 69300 | 7.5392        |
+| 0.9684 | 69400 | 7.5909        |
+| 0.9698 | 69500 | 7.5587        |
+| 0.9712 | 69600 | 7.5447        |
+| 0.9726 | 69700 | 7.5731        |
+| 0.9740 | 69800 | 7.5767        |
+| 0.9754 | 69900 | 7.6208        |
+| 0.9768 | 70000 | 7.5414        |
+| 0.9782 | 70100 | 7.6061        |
+| 0.9796 | 70200 | 7.6285        |
+| 0.9810 | 70300 | 7.5533        |
+| 0.9824 | 70400 | 7.5552        |
+| 0.9838 | 70500 | 7.5479        |
+| 0.9852 | 70600 | 7.571         |
+| 0.9865 | 70700 | 7.6259        |
+| 0.9879 | 70800 | 7.6366        |
+| 0.9893 | 70900 | 7.5615        |
+| 0.9907 | 71000 | 7.612         |
+| 0.9921 | 71100 | 7.5309        |
+| 0.9935 | 71200 | 7.5122        |
+| 0.9949 | 71300 | 7.5692        |
+| 0.9963 | 71400 | 7.6198        |
+| 0.9977 | 71500 | 7.527         |
+| 0.9991 | 71600 | 7.5496        |
+
+</details>
+
+### Framework Versions
+- Python: 3.8.10
+- Sentence Transformers: 3.1.1
+- Transformers: 4.45.2
+- PyTorch: 2.4.1+cu118
+- Accelerate: 1.0.1
+- Datasets: 3.0.1
+- Tokenizers: 0.20.3
+
+## 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 +1,26 @@
+{
+  "_name_or_path": "sentence-transformers/all-MiniLM-L6-v2",
+  "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": "4.45.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.1.1",
+    "transformers": "4.45.2",
+    "pytorch": "2.4.1+cu118"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:237c513261dfddde1c8e7aa3090f0aba61d3dd021b7873cbdfd4818d22a612fb
+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 +1,64 @@
+{
+  "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,
+  "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]"
+}