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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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+---
+base_model: sentence-transformers/all-MiniLM-L6-v2
+language:
+- en
+library_name: sentence-transformers
+license: apache-2.0
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:1363306
+- loss:CoSENTLoss
+widget:
+- source_sentence: labneh
+  sentences:
+  - iftar
+  - bathing suit
+  - coffee cup
+- source_sentence: Velvet flock Veil
+  sentences:
+  - mermaid purse
+  - veil
+  - mobile bag
+- source_sentence: Red lipstick
+  sentences:
+  - chemise dress
+  - tote
+  - rouge
+- source_sentence: Unisex Travel bag
+  sentences:
+  - spf
+  - basic vega ring
+  - travel backpack
+- source_sentence: jeremy hush book
+  sentences:
+  - chinese jumper
+  - perfume
+  - home automation device
+---
+
+# all-MiniLM-L6-v4-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 fa97f6e7cb1a59073dff9e6b13e2715cf7475ac9 -->
+- **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 = [
+    'jeremy hush book',
+    'chinese jumper',
+    'perfume',
+]
+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`: 4
+- `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`: 4
+- `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 | loss   |
+|:------:|:-----:|:-------------:|:------:|
+| 0.0094 | 100   | 17.0123       | -      |
+| 0.0188 | 200   | 16.3963       | -      |
+| 0.0282 | 300   | 14.9883       | -      |
+| 0.0376 | 400   | 12.5378       | -      |
+| 0.0469 | 500   | 9.8375        | -      |
+| 0.0563 | 600   | 8.4884        | -      |
+| 0.0657 | 700   | 8.2217        | -      |
+| 0.0751 | 800   | 8.1311        | -      |
+| 0.0845 | 900   | 8.104         | -      |
+| 0.0939 | 1000  | 8.0921        | -      |
+| 0.1033 | 1100  | 8.0568        | -      |
+| 0.1127 | 1200  | 8.0567        | -      |
+| 0.1221 | 1300  | 8.0534        | -      |
+| 0.1314 | 1400  | 8.0189        | -      |
+| 0.1408 | 1500  | 8.0172        | -      |
+| 0.1502 | 1600  | 8.0291        | -      |
+| 0.1596 | 1700  | 8.0396        | -      |
+| 0.1690 | 1800  | 8.0527        | -      |
+| 0.1784 | 1900  | 8.0543        | -      |
+| 0.1878 | 2000  | 8.0244        | -      |
+| 0.1972 | 2100  | 8.0294        | -      |
+| 0.2066 | 2200  | 8.019         | -      |
+| 0.2159 | 2300  | 7.9946        | -      |
+| 0.2253 | 2400  | 8.0233        | -      |
+| 0.2347 | 2500  | 8.0058        | -      |
+| 0.2441 | 2600  | 8.0146        | -      |
+| 0.2535 | 2700  | 8.0116        | -      |
+| 0.2629 | 2800  | 7.9843        | -      |
+| 0.2723 | 2900  | 8.0226        | -      |
+| 0.2817 | 3000  | 7.991         | -      |
+| 0.2911 | 3100  | 8.0041        | -      |
+| 0.3004 | 3200  | 8.025         | -      |
+| 0.3098 | 3300  | 7.9913        | -      |
+| 0.3192 | 3400  | 7.9852        | -      |
+| 0.3286 | 3500  | 8.0103        | -      |
+| 0.3380 | 3600  | 7.9911        | -      |
+| 0.3474 | 3700  | 7.9892        | -      |
+| 0.3568 | 3800  | 7.9605        | -      |
+| 0.3662 | 3900  | 8.011         | -      |
+| 0.3756 | 4000  | 7.9894        | -      |
+| 0.3849 | 4100  | 7.9658        | -      |
+| 0.3943 | 4200  | 7.9791        | -      |
+| 0.4037 | 4300  | 7.9717        | -      |
+| 0.4131 | 4400  | 8.0139        | -      |
+| 0.4225 | 4500  | 7.9691        | -      |
+| 0.4319 | 4600  | 8.0115        | -      |
+| 0.4413 | 4700  | 8.0245        | -      |
+| 0.4507 | 4800  | 8.0289        | -      |
+| 0.4601 | 4900  | 7.9644        | -      |
+| 0.4694 | 5000  | 7.9851        | 7.9703 |
+| 0.4788 | 5100  | 7.9594        | -      |
+| 0.4882 | 5200  | 7.9618        | -      |
+| 0.4976 | 5300  | 7.9917        | -      |
+| 0.5070 | 5400  | 7.988         | -      |
+| 0.5164 | 5500  | 8.0203        | -      |
+| 0.5258 | 5600  | 7.9738        | -      |
+| 0.5352 | 5700  | 7.9614        | -      |
+| 0.5445 | 5800  | 7.9567        | -      |
+| 0.5539 | 5900  | 7.9721        | -      |
+| 0.5633 | 6000  | 7.96          | -      |
+| 0.5727 | 6100  | 7.9376        | -      |
+| 0.5821 | 6200  | 7.9901        | -      |
+| 0.5915 | 6300  | 7.9559        | -      |
+| 0.6009 | 6400  | 7.9548        | -      |
+| 0.6103 | 6500  | 8.0004        | -      |
+| 0.6197 | 6600  | 7.9607        | -      |
+| 0.6290 | 6700  | 7.9779        | -      |
+| 0.6384 | 6800  | 7.9401        | -      |
+| 0.6478 | 6900  | 7.9695        | -      |
+| 0.6572 | 7000  | 7.9667        | -      |
+| 0.6666 | 7100  | 7.9679        | -      |
+| 0.6760 | 7200  | 7.9821        | -      |
+| 0.6854 | 7300  | 7.9981        | -      |
+| 0.6948 | 7400  | 7.975         | -      |
+| 0.7042 | 7500  | 7.9438        | -      |
+| 0.7135 | 7600  | 7.9611        | -      |
+| 0.7229 | 7700  | 7.9501        | -      |
+| 0.7323 | 7800  | 7.9565        | -      |
+| 0.7417 | 7900  | 7.9199        | -      |
+| 0.7511 | 8000  | 7.9601        | -      |
+| 0.7605 | 8100  | 7.9208        | -      |
+| 0.7699 | 8200  | 7.9488        | -      |
+| 0.7793 | 8300  | 7.9519        | -      |
+| 0.7887 | 8400  | 7.9806        | -      |
+| 0.7980 | 8500  | 7.9557        | -      |
+| 0.8074 | 8600  | 7.9383        | -      |
+| 0.8168 | 8700  | 7.9541        | -      |
+| 0.8262 | 8800  | 7.9529        | -      |
+| 0.8356 | 8900  | 7.9463        | -      |
+| 0.8450 | 9000  | 7.9674        | -      |
+| 0.8544 | 9100  | 7.9454        | -      |
+| 0.8638 | 9200  | 7.9613        | -      |
+| 0.8732 | 9300  | 7.9119        | -      |
+| 0.8825 | 9400  | 7.9806        | -      |
+| 0.8919 | 9500  | 7.9449        | -      |
+| 0.9013 | 9600  | 7.9254        | -      |
+| 0.9107 | 9700  | 7.9156        | -      |
+| 0.9201 | 9800  | 7.9105        | -      |
+| 0.9295 | 9900  | 7.9668        | -      |
+| 0.9389 | 10000 | 7.9922        | 7.9137 |
+| 0.9483 | 10100 | 7.9261        | -      |
+| 0.9577 | 10200 | 7.9134        | -      |
+| 0.9670 | 10300 | 7.8968        | -      |
+| 0.9764 | 10400 | 7.9086        | -      |
+| 0.9858 | 10500 | 7.9609        | -      |
+| 0.9952 | 10600 | 7.9125        | -      |
+| 1.0046 | 10700 | 7.8816        | -      |
+| 1.0140 | 10800 | 7.9558        | -      |
+| 1.0234 | 10900 | 7.9357        | -      |
+| 1.0328 | 11000 | 7.9212        | -      |
+| 1.0422 | 11100 | 7.9305        | -      |
+| 1.0515 | 11200 | 7.9073        | -      |
+| 1.0609 | 11300 | 7.9016        | -      |
+| 1.0703 | 11400 | 7.9321        | -      |
+| 1.0797 | 11500 | 7.8765        | -      |
+| 1.0891 | 11600 | 7.8907        | -      |
+| 1.0985 | 11700 | 7.9338        | -      |
+| 1.1079 | 11800 | 7.9163        | -      |
+| 1.1173 | 11900 | 7.8892        | -      |
+| 1.1267 | 12000 | 7.9261        | -      |
+| 1.1360 | 12100 | 7.8846        | -      |
+| 1.1454 | 12200 | 7.8976        | -      |
+| 1.1548 | 12300 | 7.8796        | -      |
+| 1.1642 | 12400 | 7.9041        | -      |
+| 1.1736 | 12500 | 7.9181        | -      |
+| 1.1830 | 12600 | 7.8944        | -      |
+| 1.1924 | 12700 | 7.9168        | -      |
+| 1.2018 | 12800 | 7.9122        | -      |
+| 1.2112 | 12900 | 7.9006        | -      |
+| 1.2205 | 13000 | 7.916         | -      |
+| 1.2299 | 13100 | 7.8994        | -      |
+| 1.2393 | 13200 | 7.8785        | -      |
+| 1.2487 | 13300 | 7.8751        | -      |
+| 1.2581 | 13400 | 7.9022        | -      |
+| 1.2675 | 13500 | 7.8806        | -      |
+| 1.2769 | 13600 | 7.9056        | -      |
+| 1.2863 | 13700 | 7.889         | -      |
+| 1.2957 | 13800 | 7.9155        | -      |
+| 1.3050 | 13900 | 7.9346        | -      |
+| 1.3144 | 14000 | 7.8537        | -      |
+| 1.3238 | 14100 | 7.8961        | -      |
+| 1.3332 | 14200 | 7.8977        | -      |
+| 1.3426 | 14300 | 7.887         | -      |
+| 1.3520 | 14400 | 7.8839        | -      |
+| 1.3614 | 14500 | 7.9331        | -      |
+| 1.3708 | 14600 | 7.8964        | -      |
+| 1.3802 | 14700 | 7.8773        | -      |
+| 1.3895 | 14800 | 7.8749        | -      |
+| 1.3989 | 14900 | 7.8824        | -      |
+| 1.4083 | 15000 | 7.8987        | 7.8832 |
+| 1.4177 | 15100 | 7.8683        | -      |
+| 1.4271 | 15200 | 7.9177        | -      |
+| 1.4365 | 15300 | 7.8573        | -      |
+| 1.4459 | 15400 | 7.8797        | -      |
+| 1.4553 | 15500 | 7.8577        | -      |
+| 1.4647 | 15600 | 7.8827        | -      |
+| 1.4740 | 15700 | 7.8548        | -      |
+| 1.4834 | 15800 | 7.906         | -      |
+| 1.4928 | 15900 | 7.8808        | -      |
+| 1.5022 | 16000 | 7.8886        | -      |
+| 1.5116 | 16100 | 7.872         | -      |
+| 1.5210 | 16200 | 7.8746        | -      |
+| 1.5304 | 16300 | 7.8855        | -      |
+| 1.5398 | 16400 | 7.8549        | -      |
+| 1.5492 | 16500 | 7.8727        | -      |
+| 1.5585 | 16600 | 7.887         | -      |
+| 1.5679 | 16700 | 7.8534        | -      |
+| 1.5773 | 16800 | 7.888         | -      |
+| 1.5867 | 16900 | 7.8525        | -      |
+| 1.5961 | 17000 | 7.8818        | -      |
+| 1.6055 | 17100 | 7.9097        | -      |
+| 1.6149 | 17200 | 7.855         | -      |
+| 1.6243 | 17300 | 7.8925        | -      |
+| 1.6336 | 17400 | 7.8652        | -      |
+| 1.6430 | 17500 | 7.866         | -      |
+| 1.6524 | 17600 | 7.8411        | -      |
+| 1.6618 | 17700 | 7.8525        | -      |
+| 1.6712 | 17800 | 7.8651        | -      |
+| 1.6806 | 17900 | 7.8411        | -      |
+| 1.6900 | 18000 | 7.8622        | -      |
+| 1.6994 | 18100 | 7.8833        | -      |
+| 1.7088 | 18200 | 7.9135        | -      |
+| 1.7181 | 18300 | 7.8527        | -      |
+| 1.7275 | 18400 | 7.8451        | -      |
+| 1.7369 | 18500 | 7.8766        | -      |
+| 1.7463 | 18600 | 7.8375        | -      |
+| 1.7557 | 18700 | 7.8433        | -      |
+| 1.7651 | 18800 | 7.8321        | -      |
+| 1.7745 | 18900 | 7.8594        | -      |
+| 1.7839 | 19000 | 7.8398        | -      |
+| 1.7933 | 19100 | 7.8764        | -      |
+| 1.8026 | 19200 | 7.841         | -      |
+| 1.8120 | 19300 | 7.8515        | -      |
+| 1.8214 | 19400 | 7.8458        | -      |
+| 1.8308 | 19500 | 7.8409        | -      |
+| 1.8402 | 19600 | 7.8768        | -      |
+| 1.8496 | 19700 | 7.8533        | -      |
+| 1.8590 | 19800 | 7.8538        | -      |
+| 1.8684 | 19900 | 7.8547        | -      |
+| 1.8778 | 20000 | 7.8522        | 7.8474 |
+| 1.8871 | 20100 | 7.87          | -      |
+| 1.8965 | 20200 | 7.8586        | -      |
+| 1.9059 | 20300 | 7.8529        | -      |
+| 1.9153 | 20400 | 7.8373        | -      |
+| 1.9247 | 20500 | 7.8239        | -      |
+| 1.9341 | 20600 | 7.8782        | -      |
+| 1.9435 | 20700 | 7.8533        | -      |
+| 1.9529 | 20800 | 7.8403        | -      |
+| 1.9623 | 20900 | 7.8904        | -      |
+| 1.9716 | 21000 | 7.8287        | -      |
+| 1.9810 | 21100 | 7.8844        | -      |
+| 1.9904 | 21200 | 7.8625        | -      |
+| 1.9998 | 21300 | 7.8568        | -      |
+| 2.0092 | 21400 | 7.841         | -      |
+| 2.0186 | 21500 | 7.8214        | -      |
+| 2.0280 | 21600 | 7.8255        | -      |
+| 2.0374 | 21700 | 7.8196        | -      |
+| 2.0468 | 21800 | 7.8441        | -      |
+| 2.0561 | 21900 | 7.8785        | -      |
+| 2.0655 | 22000 | 7.8331        | -      |
+| 2.0749 | 22100 | 7.8516        | -      |
+| 2.0843 | 22200 | 7.8164        | -      |
+| 2.0937 | 22300 | 7.8206        | -      |
+| 2.1031 | 22400 | 7.815         | -      |
+| 2.1125 | 22500 | 7.8048        | -      |
+| 2.1219 | 22600 | 7.8218        | -      |
+| 2.1313 | 22700 | 7.8371        | -      |
+| 2.1406 | 22800 | 7.7967        | -      |
+| 2.1500 | 22900 | 7.8182        | -      |
+| 2.1594 | 23000 | 7.8352        | -      |
+| 2.1688 | 23100 | 7.8565        | -      |
+| 2.1782 | 23200 | 7.8293        | -      |
+| 2.1876 | 23300 | 7.8216        | -      |
+| 2.1970 | 23400 | 7.8155        | -      |
+| 2.2064 | 23500 | 7.8269        | -      |
+| 2.2158 | 23600 | 7.8378        | -      |
+| 2.2251 | 23700 | 7.8056        | -      |
+| 2.2345 | 23800 | 7.827         | -      |
+| 2.2439 | 23900 | 7.8095        | -      |
+| 2.2533 | 24000 | 7.8292        | -      |
+| 2.2627 | 24100 | 7.8349        | -      |
+| 2.2721 | 24200 | 7.8391        | -      |
+| 2.2815 | 24300 | 7.8161        | -      |
+| 2.2909 | 24400 | 7.8053        | -      |
+| 2.3003 | 24500 | 7.8641        | -      |
+| 2.3096 | 24600 | 7.855         | -      |
+| 2.3190 | 24700 | 7.8286        | -      |
+| 2.3284 | 24800 | 7.8605        | -      |
+| 2.3378 | 24900 | 7.828         | -      |
+| 2.3472 | 25000 | 7.8274        | 7.8454 |
+| 2.3566 | 25100 | 7.8104        | -      |
+| 2.3660 | 25200 | 7.873         | -      |
+| 2.3754 | 25300 | 7.7956        | -      |
+| 2.3848 | 25400 | 7.8135        | -      |
+| 2.3941 | 25500 | 7.8033        | -      |
+| 2.4035 | 25600 | 7.812         | -      |
+| 2.4129 | 25700 | 7.8285        | -      |
+| 2.4223 | 25800 | 7.8062        | -      |
+| 2.4317 | 25900 | 7.8178        | -      |
+| 2.4411 | 26000 | 7.8051        | -      |
+| 2.4505 | 26100 | 7.8255        | -      |
+| 2.4599 | 26200 | 7.8026        | -      |
+| 2.4693 | 26300 | 7.8627        | -      |
+| 2.4786 | 26400 | 7.8018        | -      |
+| 2.4880 | 26500 | 7.787         | -      |
+| 2.4974 | 26600 | 7.8374        | -      |
+| 2.5068 | 26700 | 7.8227        | -      |
+| 2.5162 | 26800 | 7.8076        | -      |
+| 2.5256 | 26900 | 7.7875        | -      |
+| 2.5350 | 27000 | 7.7908        | -      |
+| 2.5444 | 27100 | 7.8162        | -      |
+| 2.5538 | 27200 | 7.7919        | -      |
+| 2.5631 | 27300 | 7.8033        | -      |
+| 2.5725 | 27400 | 7.8147        | -      |
+| 2.5819 | 27500 | 7.8013        | -      |
+| 2.5913 | 27600 | 7.777         | -      |
+| 2.6007 | 27700 | 7.7982        | -      |
+| 2.6101 | 27800 | 7.8025        | -      |
+| 2.6195 | 27900 | 7.79          | -      |
+| 2.6289 | 28000 | 7.8124        | -      |
+| 2.6382 | 28100 | 7.7936        | -      |
+| 2.6476 | 28200 | 7.7793        | -      |
+| 2.6570 | 28300 | 7.8126        | -      |
+| 2.6664 | 28400 | 7.8149        | -      |
+| 2.6758 | 28500 | 7.7919        | -      |
+| 2.6852 | 28600 | 7.8127        | -      |
+| 2.6946 | 28700 | 7.8339        | -      |
+| 2.7040 | 28800 | 7.805         | -      |
+| 2.7134 | 28900 | 7.794         | -      |
+| 2.7227 | 29000 | 7.777         | -      |
+| 2.7321 | 29100 | 7.7888        | -      |
+| 2.7415 | 29200 | 7.8384        | -      |
+| 2.7509 | 29300 | 7.8175        | -      |
+| 2.7603 | 29400 | 7.8394        | -      |
+| 2.7697 | 29500 | 7.7813        | -      |
+| 2.7791 | 29600 | 7.8205        | -      |
+| 2.7885 | 29700 | 7.7982        | -      |
+| 2.7979 | 29800 | 7.7904        | -      |
+| 2.8072 | 29900 | 7.8107        | -      |
+| 2.8166 | 30000 | 7.8217        | 7.8158 |
+| 2.8260 | 30100 | 7.7893        | -      |
+| 2.8354 | 30200 | 7.8139        | -      |
+| 2.8448 | 30300 | 7.8097        | -      |
+| 2.8542 | 30400 | 7.7966        | -      |
+| 2.8636 | 30500 | 7.7895        | -      |
+| 2.8730 | 30600 | 7.7914        | -      |
+| 2.8824 | 30700 | 7.8095        | -      |
+| 2.8917 | 30800 | 7.7943        | -      |
+| 2.9011 | 30900 | 7.8001        | -      |
+| 2.9105 | 31000 | 7.8299        | -      |
+| 2.9199 | 31100 | 7.7804        | -      |
+| 2.9293 | 31200 | 7.8015        | -      |
+| 2.9387 | 31300 | 7.8038        | -      |
+| 2.9481 | 31400 | 7.7731        | -      |
+| 2.9575 | 31500 | 7.7856        | -      |
+| 2.9669 | 31600 | 7.7935        | -      |
+| 2.9762 | 31700 | 7.7896        | -      |
+| 2.9856 | 31800 | 7.8216        | -      |
+| 2.9950 | 31900 | 7.7841        | -      |
+| 3.0044 | 32000 | 7.7569        | -      |
+| 3.0138 | 32100 | 7.7929        | -      |
+| 3.0232 | 32200 | 7.7738        | -      |
+| 3.0326 | 32300 | 7.7837        | -      |
+| 3.0420 | 32400 | 7.7777        | -      |
+| 3.0514 | 32500 | 7.7829        | -      |
+| 3.0607 | 32600 | 7.7585        | -      |
+| 3.0701 | 32700 | 7.7896        | -      |
+| 3.0795 | 32800 | 7.7873        | -      |
+| 3.0889 | 32900 | 7.7904        | -      |
+| 3.0983 | 33000 | 7.7808        | -      |
+| 3.1077 | 33100 | 7.7871        | -      |
+| 3.1171 | 33200 | 7.7835        | -      |
+| 3.1265 | 33300 | 7.7819        | -      |
+| 3.1359 | 33400 | 7.8037        | -      |
+| 3.1452 | 33500 | 7.7585        | -      |
+| 3.1546 | 33600 | 7.7928        | -      |
+| 3.1640 | 33700 | 7.7751        | -      |
+| 3.1734 | 33800 | 7.7829        | -      |
+| 3.1828 | 33900 | 7.7723        | -      |
+| 3.1922 | 34000 | 7.7999        | -      |
+| 3.2016 | 34100 | 7.757         | -      |
+| 3.2110 | 34200 | 7.7682        | -      |
+| 3.2204 | 34300 | 7.784         | -      |
+| 3.2297 | 34400 | 7.7962        | -      |
+| 3.2391 | 34500 | 7.7913        | -      |
+| 3.2485 | 34600 | 7.7768        | -      |
+| 3.2579 | 34700 | 7.7749        | -      |
+| 3.2673 | 34800 | 7.7724        | -      |
+| 3.2767 | 34900 | 7.7786        | -      |
+| 3.2861 | 35000 | 7.775         | 7.8301 |
+| 3.2955 | 35100 | 7.7702        | -      |
+| 3.3049 | 35200 | 7.7689        | -      |
+| 3.3142 | 35300 | 7.7676        | -      |
+| 3.3236 | 35400 | 7.8029        | -      |
+| 3.3330 | 35500 | 7.7945        | -      |
+| 3.3424 | 35600 | 7.7765        | -      |
+| 3.3518 | 35700 | 7.7799        | -      |
+| 3.3612 | 35800 | 7.7701        | -      |
+| 3.3706 | 35900 | 7.7572        | -      |
+| 3.3800 | 36000 | 7.7656        | -      |
+| 3.3894 | 36100 | 7.8075        | -      |
+| 3.3987 | 36200 | 7.771         | -      |
+| 3.4081 | 36300 | 7.7757        | -      |
+| 3.4175 | 36400 | 7.7713        | -      |
+| 3.4269 | 36500 | 7.7885        | -      |
+| 3.4363 | 36600 | 7.7547        | -      |
+| 3.4457 | 36700 | 7.761         | -      |
+| 3.4551 | 36800 | 7.7797        | -      |
+| 3.4645 | 36900 | 7.7576        | -      |
+| 3.4739 | 37000 | 7.7578        | -      |
+| 3.4832 | 37100 | 7.736         | -      |
+| 3.4926 | 37200 | 7.7532        | -      |
+| 3.5020 | 37300 | 7.7747        | -      |
+| 3.5114 | 37400 | 7.7578        | -      |
+| 3.5208 | 37500 | 7.7632        | -      |
+| 3.5302 | 37600 | 7.7689        | -      |
+| 3.5396 | 37700 | 7.7796        | -      |
+| 3.5490 | 37800 | 7.7897        | -      |
+| 3.5584 | 37900 | 7.7824        | -      |
+| 3.5677 | 38000 | 7.7479        | -      |
+| 3.5771 | 38100 | 7.781         | -      |
+| 3.5865 | 38200 | 7.769         | -      |
+| 3.5959 | 38300 | 7.8087        | -      |
+| 3.6053 | 38400 | 7.7742        | -      |
+| 3.6147 | 38500 | 7.7974        | -      |
+| 3.6241 | 38600 | 7.7661        | -      |
+| 3.6335 | 38700 | 7.758         | -      |
+| 3.6429 | 38800 | 7.7659        | -      |
+| 3.6522 | 38900 | 7.753         | -      |
+| 3.6616 | 39000 | 7.819         | -      |
+| 3.6710 | 39100 | 7.766         | -      |
+| 3.6804 | 39200 | 7.7649        | -      |
+| 3.6898 | 39300 | 7.7684        | -      |
+| 3.6992 | 39400 | 7.7716        | -      |
+| 3.7086 | 39500 | 7.7781        | -      |
+| 3.7180 | 39600 | 7.788         | -      |
+| 3.7273 | 39700 | 7.7834        | -      |
+| 3.7367 | 39800 | 7.7566        | -      |
+| 3.7461 | 39900 | 7.7567        | -      |
+| 3.7555 | 40000 | 7.7804        | 7.8189 |
+| 3.7649 | 40100 | 7.7559        | -      |
+| 3.7743 | 40200 | 7.7793        | -      |
+| 3.7837 | 40300 | 7.7749        | -      |
+| 3.7931 | 40400 | 7.7773        | -      |
+| 3.8025 | 40500 | 7.7836        | -      |
+| 3.8118 | 40600 | 7.7817        | -      |
+| 3.8212 | 40700 | 7.8036        | -      |
+| 3.8306 | 40800 | 7.7693        | -      |
+| 3.8400 | 40900 | 7.7895        | -      |
+| 3.8494 | 41000 | 7.789         | -      |
+| 3.8588 | 41100 | 7.7432        | -      |
+| 3.8682 | 41200 | 7.7777        | -      |
+| 3.8776 | 41300 | 7.7399        | -      |
+| 3.8870 | 41400 | 7.7629        | -      |
+| 3.8963 | 41500 | 7.7578        | -      |
+| 3.9057 | 41600 | 7.787         | -      |
+| 3.9151 | 41700 | 7.7984        | -      |
+| 3.9245 | 41800 | 7.7842        | -      |
+| 3.9339 | 41900 | 7.7992        | -      |
+| 3.9433 | 42000 | 7.7921        | -      |
+| 3.9527 | 42100 | 7.7986        | -      |
+| 3.9621 | 42200 | 7.7571        | -      |
+| 3.9715 | 42300 | 7.7783        | -      |
+| 3.9808 | 42400 | 7.7636        | -      |
+| 3.9902 | 42500 | 7.7633        | -      |
+| 3.9996 | 42600 | 7.7673        | -      |
+
+</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 sha256:5c88c0042b88b6a3dfe94216d5ee484ea7caacc2195ea4b7c7b731333bf3b590
+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]"
+}