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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:7747936
+- loss:CoSENTLoss
+widget:
+- source_sentence: mango cake cream cake sponge cake gateau mango gateau cream gateau
+    mango sponge cake cream sponge cake mango cream cake mango cream sponge cake mango
+    flavored sponge cake layers cream filling decorated with fresh mango slices topped
+    with whipped cream serves 10 people mango cream cake sponge cake gateau mango
+    gateau with cream filling whipped cream mango cake mango cream sponge cake for
+    10 people
+  sentences:
+  - vegan dessert
+  - oxidized ring
+  - cola lip gloss
+- source_sentence: double breasted blouse
+  sentences:
+  - brushed jersey sweatshirt
+  - comfort facial tissues
+  - round neck sweatshirt
+- source_sentence: casual shirt
+  sentences:
+  - adjustable string top
+  - foldable spare backpack
+  - spring blossom scent shower gel
+- source_sentence: sweet chilli mozzarella stick
+  sentences:
+  - fragrance free facial cream
+  - outdoor basket
+  - cobb dressing salad
+- source_sentence: appetizer onion ring
+  sentences:
+  - high quality sports bra
+  - swimmer burkini
+  - nuttella pizza
+---
+
+# all-MiniLM-L6-v10-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 = [
+    'appetizer onion ring',
+    'nuttella pizza',
+    'high quality sports bra',
+]
+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 |
+|:------:|:-----:|:-------------:|
+| 0.0017 | 100   | 13.3171       |
+| 0.0033 | 200   | 12.9799       |
+| 0.0050 | 300   | 12.5133       |
+| 0.0066 | 400   | 11.9388       |
+| 0.0083 | 500   | 11.0616       |
+| 0.0099 | 600   | 10.2712       |
+| 0.0116 | 700   | 9.5253        |
+| 0.0132 | 800   | 8.7706        |
+| 0.0149 | 900   | 8.4333        |
+| 0.0165 | 1000  | 8.0902        |
+| 0.0182 | 1100  | 7.8862        |
+| 0.0198 | 1200  | 7.7362        |
+| 0.0215 | 1300  | 7.6007        |
+| 0.0231 | 1400  | 7.5304        |
+| 0.0248 | 1500  | 7.4249        |
+| 0.0264 | 1600  | 7.3035        |
+| 0.0281 | 1700  | 7.2026        |
+| 0.0297 | 1800  | 7.1572        |
+| 0.0314 | 1900  | 7.0523        |
+| 0.0330 | 2000  | 7.1158        |
+| 0.0347 | 2100  | 6.9856        |
+| 0.0363 | 2200  | 7.0865        |
+| 0.0380 | 2300  | 6.9496        |
+| 0.0396 | 2400  | 6.9294        |
+| 0.0413 | 2500  | 6.8825        |
+| 0.0430 | 2600  | 6.8218        |
+| 0.0446 | 2700  | 6.8416        |
+| 0.0463 | 2800  | 6.7184        |
+| 0.0479 | 2900  | 6.9183        |
+| 0.0496 | 3000  | 6.7166        |
+| 0.0512 | 3100  | 6.6821        |
+| 0.0529 | 3200  | 6.6074        |
+| 0.0545 | 3300  | 6.6141        |
+| 0.0562 | 3400  | 6.5374        |
+| 0.0578 | 3500  | 6.4776        |
+| 0.0595 | 3600  | 6.5701        |
+| 0.0611 | 3700  | 6.5026        |
+| 0.0628 | 3800  | 6.6502        |
+| 0.0644 | 3900  | 6.5023        |
+| 0.0661 | 4000  | 6.5526        |
+| 0.0677 | 4100  | 6.6594        |
+| 0.0694 | 4200  | 6.3643        |
+| 0.0710 | 4300  | 6.3783        |
+| 0.0727 | 4400  | 6.3222        |
+| 0.0743 | 4500  | 6.3401        |
+| 0.0760 | 4600  | 6.4005        |
+| 0.0776 | 4700  | 6.3605        |
+| 0.0793 | 4800  | 6.348         |
+| 0.0810 | 4900  | 6.3406        |
+| 0.0826 | 5000  | 6.4156        |
+| 0.0843 | 5100  | 6.3786        |
+| 0.0859 | 5200  | 6.376         |
+| 0.0876 | 5300  | 6.2363        |
+| 0.0892 | 5400  | 6.2185        |
+| 0.0909 | 5500  | 6.2554        |
+| 0.0925 | 5600  | 6.2177        |
+| 0.0942 | 5700  | 6.3924        |
+| 0.0958 | 5800  | 6.2897        |
+| 0.0975 | 5900  | 6.272         |
+| 0.0991 | 6000  | 6.0247        |
+| 0.1008 | 6100  | 6.194         |
+| 0.1024 | 6200  | 6.2757        |
+| 0.1041 | 6300  | 6.2408        |
+| 0.1057 | 6400  | 6.253         |
+| 0.1074 | 6500  | 6.0605        |
+| 0.1090 | 6600  | 6.0672        |
+| 0.1107 | 6700  | 6.0414        |
+| 0.1123 | 6800  | 6.0823        |
+| 0.1140 | 6900  | 6.1962        |
+| 0.1156 | 7000  | 6.0868        |
+| 0.1173 | 7100  | 6.0795        |
+| 0.1189 | 7200  | 5.9656        |
+| 0.1206 | 7300  | 5.9785        |
+| 0.1223 | 7400  | 6.0722        |
+| 0.1239 | 7500  | 5.9443        |
+| 0.1256 | 7600  | 5.8786        |
+| 0.1272 | 7700  | 5.8007        |
+| 0.1289 | 7800  | 5.9206        |
+| 0.1305 | 7900  | 5.918         |
+| 0.1322 | 8000  | 5.9443        |
+| 0.1338 | 8100  | 5.8764        |
+| 0.1355 | 8200  | 5.867         |
+| 0.1371 | 8300  | 5.8087        |
+| 0.1388 | 8400  | 5.9884        |
+| 0.1404 | 8500  | 5.8741        |
+| 0.1421 | 8600  | 5.9699        |
+| 0.1437 | 8700  | 5.8671        |
+| 0.1454 | 8800  | 5.8278        |
+| 0.1470 | 8900  | 5.8892        |
+| 0.1487 | 9000  | 5.7437        |
+| 0.1503 | 9100  | 5.8069        |
+| 0.1520 | 9200  | 6.0235        |
+| 0.1536 | 9300  | 5.7214        |
+| 0.1553 | 9400  | 5.7893        |
+| 0.1569 | 9500  | 5.7406        |
+| 0.1586 | 9600  | 5.8035        |
+| 0.1602 | 9700  | 5.7965        |
+| 0.1619 | 9800  | 5.638         |
+| 0.1636 | 9900  | 5.8263        |
+| 0.1652 | 10000 | 5.7995        |
+| 0.1669 | 10100 | 5.5805        |
+| 0.1685 | 10200 | 5.632         |
+| 0.1702 | 10300 | 5.6944        |
+| 0.1718 | 10400 | 5.5818        |
+| 0.1735 | 10500 | 5.8598        |
+| 0.1751 | 10600 | 5.7255        |
+| 0.1768 | 10700 | 5.7536        |
+| 0.1784 | 10800 | 5.6536        |
+| 0.1801 | 10900 | 5.6417        |
+| 0.1817 | 11000 | 5.6719        |
+| 0.1834 | 11100 | 5.566         |
+| 0.1850 | 11200 | 5.4893        |
+| 0.1867 | 11300 | 5.7412        |
+| 0.1883 | 11400 | 5.6838        |
+| 0.1900 | 11500 | 5.6272        |
+| 0.1916 | 11600 | 5.6538        |
+| 0.1933 | 11700 | 5.7176        |
+| 0.1949 | 11800 | 5.4923        |
+| 0.1966 | 11900 | 5.7643        |
+| 0.1982 | 12000 | 5.5674        |
+| 0.1999 | 12100 | 5.6896        |
+| 0.2015 | 12200 | 5.4385        |
+| 0.2032 | 12300 | 5.5851        |
+| 0.2049 | 12400 | 5.5132        |
+| 0.2065 | 12500 | 5.3329        |
+| 0.2082 | 12600 | 5.4218        |
+| 0.2098 | 12700 | 5.5171        |
+| 0.2115 | 12800 | 5.3414        |
+| 0.2131 | 12900 | 5.4921        |
+| 0.2148 | 13000 | 5.7687        |
+| 0.2164 | 13100 | 5.7119        |
+| 0.2181 | 13200 | 5.4975        |
+| 0.2197 | 13300 | 5.4514        |
+| 0.2214 | 13400 | 5.497         |
+| 0.2230 | 13500 | 5.558         |
+| 0.2247 | 13600 | 5.4207        |
+| 0.2263 | 13700 | 5.5901        |
+| 0.2280 | 13800 | 5.2041        |
+| 0.2296 | 13900 | 5.2999        |
+| 0.2313 | 14000 | 5.3373        |
+| 0.2329 | 14100 | 5.789         |
+| 0.2346 | 14200 | 5.3292        |
+| 0.2362 | 14300 | 5.4059        |
+| 0.2379 | 14400 | 5.1849        |
+| 0.2395 | 14500 | 5.1262        |
+| 0.2412 | 14600 | 5.4339        |
+| 0.2429 | 14700 | 5.5185        |
+| 0.2445 | 14800 | 5.3286        |
+| 0.2462 | 14900 | 5.4141        |
+| 0.2478 | 15000 | 5.3554        |
+| 0.2495 | 15100 | 5.3489        |
+| 0.2511 | 15200 | 5.4849        |
+| 0.2528 | 15300 | 5.3656        |
+| 0.2544 | 15400 | 5.32          |
+| 0.2561 | 15500 | 5.3523        |
+| 0.2577 | 15600 | 5.1146        |
+| 0.2594 | 15700 | 5.2816        |
+| 0.2610 | 15800 | 5.2296        |
+| 0.2627 | 15900 | 5.3386        |
+| 0.2643 | 16000 | 5.4917        |
+| 0.2660 | 16100 | 5.0524        |
+| 0.2676 | 16200 | 5.1657        |
+| 0.2693 | 16300 | 5.1431        |
+| 0.2709 | 16400 | 5.166         |
+| 0.2726 | 16500 | 5.5738        |
+| 0.2742 | 16600 | 5.2088        |
+| 0.2759 | 16700 | 5.2198        |
+| 0.2775 | 16800 | 5.2709        |
+| 0.2792 | 16900 | 5.4027        |
+| 0.2808 | 17000 | 5.25          |
+| 0.2825 | 17100 | 5.1519        |
+| 0.2842 | 17200 | 5.1347        |
+| 0.2858 | 17300 | 5.2346        |
+| 0.2875 | 17400 | 5.4128        |
+| 0.2891 | 17500 | 5.1954        |
+| 0.2908 | 17600 | 5.3787        |
+| 0.2924 | 17700 | 5.1731        |
+| 0.2941 | 17800 | 5.3714        |
+| 0.2957 | 17900 | 5.2113        |
+| 0.2974 | 18000 | 5.0819        |
+| 0.2990 | 18100 | 5.0443        |
+| 0.3007 | 18200 | 5.2041        |
+| 0.3023 | 18300 | 5.1385        |
+| 0.3040 | 18400 | 5.2195        |
+| 0.3056 | 18500 | 5.2233        |
+| 0.3073 | 18600 | 5.1198        |
+| 0.3089 | 18700 | 5.106         |
+| 0.3106 | 18800 | 5.335         |
+| 0.3122 | 18900 | 5.1231        |
+| 0.3139 | 19000 | 5.1777        |
+| 0.3155 | 19100 | 5.5752        |
+| 0.3172 | 19200 | 5.1902        |
+| 0.3188 | 19300 | 5.0777        |
+| 0.3205 | 19400 | 5.211         |
+| 0.3221 | 19500 | 5.1402        |
+| 0.3238 | 19600 | 5.1458        |
+| 0.3255 | 19700 | 5.1091        |
+| 0.3271 | 19800 | 5.1471        |
+| 0.3288 | 19900 | 5.1804        |
+| 0.3304 | 20000 | 4.9678        |
+| 0.3321 | 20100 | 5.1655        |
+| 0.3337 | 20200 | 4.9735        |
+| 0.3354 | 20300 | 5.0536        |
+| 0.3370 | 20400 | 5.347         |
+| 0.3387 | 20500 | 4.9856        |
+| 0.3403 | 20600 | 5.1035        |
+| 0.3420 | 20700 | 5.0428        |
+| 0.3436 | 20800 | 5.0856        |
+| 0.3453 | 20900 | 5.0776        |
+| 0.3469 | 21000 | 5.2031        |
+| 0.3486 | 21100 | 5.1491        |
+| 0.3502 | 21200 | 5.3685        |
+| 0.3519 | 21300 | 4.6901        |
+| 0.3535 | 21400 | 4.9809        |
+| 0.3552 | 21500 | 4.9273        |
+| 0.3568 | 21600 | 4.7568        |
+| 0.3585 | 21700 | 4.9064        |
+| 0.3601 | 21800 | 5.0399        |
+| 0.3618 | 21900 | 4.9202        |
+| 0.3635 | 22000 | 5.3848        |
+| 0.3651 | 22100 | 4.9239        |
+| 0.3668 | 22200 | 4.8744        |
+| 0.3684 | 22300 | 4.8597        |
+| 0.3701 | 22400 | 4.9226        |
+| 0.3717 | 22500 | 5.0358        |
+| 0.3734 | 22600 | 4.9895        |
+| 0.3750 | 22700 | 5.004         |
+| 0.3767 | 22800 | 5.0441        |
+| 0.3783 | 22900 | 4.8129        |
+| 0.3800 | 23000 | 4.7954        |
+| 0.3816 | 23100 | 4.8156        |
+| 0.3833 | 23200 | 5.0714        |
+| 0.3849 | 23300 | 4.8543        |
+| 0.3866 | 23400 | 5.1728        |
+| 0.3882 | 23500 | 5.1891        |
+| 0.3899 | 23600 | 5.087         |
+| 0.3915 | 23700 | 4.9069        |
+| 0.3932 | 23800 | 4.9357        |
+| 0.3948 | 23900 | 4.8324        |
+| 0.3965 | 24000 | 4.8091        |
+| 0.3981 | 24100 | 4.7944        |
+| 0.3998 | 24200 | 5.0023        |
+| 0.4014 | 24300 | 4.8745        |
+| 0.4031 | 24400 | 5.0884        |
+| 0.4048 | 24500 | 5.0468        |
+| 0.4064 | 24600 | 4.8575        |
+| 0.4081 | 24700 | 4.7555        |
+| 0.4097 | 24800 | 4.6052        |
+| 0.4114 | 24900 | 4.8935        |
+| 0.4130 | 25000 | 4.8049        |
+| 0.4147 | 25100 | 4.9014        |
+| 0.4163 | 25200 | 4.7199        |
+| 0.4180 | 25300 | 4.6999        |
+| 0.4196 | 25400 | 4.6417        |
+| 0.4213 | 25500 | 5.2115        |
+| 0.4229 | 25600 | 4.9171        |
+| 0.4246 | 25700 | 4.9448        |
+| 0.4262 | 25800 | 4.6811        |
+| 0.4279 | 25900 | 5.1181        |
+| 0.4295 | 26000 | 4.8061        |
+| 0.4312 | 26100 | 4.815         |
+| 0.4328 | 26200 | 4.7731        |
+| 0.4345 | 26300 | 4.7304        |
+| 0.4361 | 26400 | 4.9838        |
+| 0.4378 | 26500 | 4.7998        |
+| 0.4394 | 26600 | 4.6946        |
+| 0.4411 | 26700 | 4.7755        |
+| 0.4427 | 26800 | 4.7347        |
+| 0.4444 | 26900 | 4.8356        |
+| 0.4461 | 27000 | 4.8642        |
+| 0.4477 | 27100 | 4.9273        |
+| 0.4494 | 27200 | 4.7114        |
+| 0.4510 | 27300 | 4.6088        |
+| 0.4527 | 27400 | 4.5046        |
+| 0.4543 | 27500 | 4.4516        |
+| 0.4560 | 27600 | 4.7491        |
+| 0.4576 | 27700 | 4.943         |
+| 0.4593 | 27800 | 4.877         |
+| 0.4609 | 27900 | 4.6912        |
+| 0.4626 | 28000 | 4.8373        |
+| 0.4642 | 28100 | 5.0152        |
+| 0.4659 | 28200 | 4.7008        |
+| 0.4675 | 28300 | 4.7549        |
+| 0.4692 | 28400 | 4.5287        |
+| 0.4708 | 28500 | 4.8211        |
+| 0.4725 | 28600 | 4.775         |
+| 0.4741 | 28700 | 4.6977        |
+| 0.4758 | 28800 | 4.9122        |
+| 0.4774 | 28900 | 4.9067        |
+| 0.4791 | 29000 | 4.8326        |
+| 0.4807 | 29100 | 4.4536        |
+| 0.4824 | 29200 | 5.0073        |
+| 0.4840 | 29300 | 4.5887        |
+| 0.4857 | 29400 | 4.7829        |
+| 0.4874 | 29500 | 4.6503        |
+| 0.4890 | 29600 | 4.5202        |
+| 0.4907 | 29700 | 4.9086        |
+| 0.4923 | 29800 | 4.743         |
+| 0.4940 | 29900 | 4.7819        |
+| 0.4956 | 30000 | 4.6159        |
+| 0.4973 | 30100 | 5.015         |
+| 0.4989 | 30200 | 4.5351        |
+| 0.5006 | 30300 | 5.0421        |
+| 0.5022 | 30400 | 4.5394        |
+| 0.5039 | 30500 | 4.7516        |
+| 0.5055 | 30600 | 4.9236        |
+| 0.5072 | 30700 | 4.833         |
+| 0.5088 | 30800 | 4.5406        |
+| 0.5105 | 30900 | 4.7325        |
+| 0.5121 | 31000 | 4.6807        |
+| 0.5138 | 31100 | 4.6052        |
+| 0.5154 | 31200 | 4.7922        |
+| 0.5171 | 31300 | 4.5013        |
+| 0.5187 | 31400 | 4.6579        |
+| 0.5204 | 31500 | 4.5152        |
+| 0.5220 | 31600 | 4.535         |
+| 0.5237 | 31700 | 4.4473        |
+| 0.5254 | 31800 | 5.0363        |
+| 0.5270 | 31900 | 4.4849        |
+| 0.5287 | 32000 | 4.6337        |
+| 0.5303 | 32100 | 4.3874        |
+| 0.5320 | 32200 | 4.6289        |
+| 0.5336 | 32300 | 4.5746        |
+| 0.5353 | 32400 | 4.7222        |
+| 0.5369 | 32500 | 4.3974        |
+| 0.5386 | 32600 | 4.8369        |
+| 0.5402 | 32700 | 4.6921        |
+| 0.5419 | 32800 | 4.603         |
+| 0.5435 | 32900 | 4.4542        |
+| 0.5452 | 33000 | 4.6976        |
+| 0.5468 | 33100 | 4.5403        |
+| 0.5485 | 33200 | 4.7398        |
+| 0.5501 | 33300 | 4.9736        |
+| 0.5518 | 33400 | 4.6373        |
+| 0.5534 | 33500 | 4.7195        |
+| 0.5551 | 33600 | 4.4237        |
+| 0.5567 | 33700 | 4.4319        |
+| 0.5584 | 33800 | 4.6785        |
+| 0.5600 | 33900 | 4.6265        |
+| 0.5617 | 34000 | 4.8585        |
+| 0.5633 | 34100 | 4.7605        |
+| 0.5650 | 34200 | 4.5328        |
+| 0.5667 | 34300 | 4.4722        |
+| 0.5683 | 34400 | 4.5651        |
+| 0.5700 | 34500 | 4.5748        |
+| 0.5716 | 34600 | 4.4733        |
+| 0.5733 | 34700 | 4.5675        |
+| 0.5749 | 34800 | 4.7731        |
+| 0.5766 | 34900 | 4.5179        |
+| 0.5782 | 35000 | 4.5138        |
+| 0.5799 | 35100 | 4.4146        |
+| 0.5815 | 35200 | 4.3349        |
+| 0.5832 | 35300 | 4.6789        |
+| 0.5848 | 35400 | 4.6405        |
+| 0.5865 | 35500 | 4.6118        |
+| 0.5881 | 35600 | 4.5165        |
+| 0.5898 | 35700 | 4.5453        |
+| 0.5914 | 35800 | 4.5286        |
+| 0.5931 | 35900 | 4.4041        |
+| 0.5947 | 36000 | 4.5261        |
+| 0.5964 | 36100 | 4.3889        |
+| 0.5980 | 36200 | 4.4186        |
+| 0.5997 | 36300 | 4.7924        |
+| 0.6013 | 36400 | 4.6042        |
+| 0.6030 | 36500 | 4.8725        |
+| 0.6046 | 36600 | 4.509         |
+| 0.6063 | 36700 | 4.3407        |
+| 0.6080 | 36800 | 4.5877        |
+| 0.6096 | 36900 | 4.6656        |
+| 0.6113 | 37000 | 4.405         |
+| 0.6129 | 37100 | 4.3588        |
+| 0.6146 | 37200 | 4.7821        |
+| 0.6162 | 37300 | 4.4748        |
+| 0.6179 | 37400 | 4.6611        |
+| 0.6195 | 37500 | 4.6503        |
+| 0.6212 | 37600 | 4.3817        |
+| 0.6228 | 37700 | 4.3708        |
+| 0.6245 | 37800 | 4.3686        |
+| 0.6261 | 37900 | 4.2679        |
+| 0.6278 | 38000 | 4.4258        |
+| 0.6294 | 38100 | 4.1701        |
+| 0.6311 | 38200 | 4.3627        |
+| 0.6327 | 38300 | 4.4051        |
+| 0.6344 | 38400 | 4.4693        |
+| 0.6360 | 38500 | 4.3831        |
+| 0.6377 | 38600 | 4.0856        |
+| 0.6393 | 38700 | 4.7917        |
+| 0.6410 | 38800 | 4.4803        |
+| 0.6426 | 38900 | 4.7869        |
+| 0.6443 | 39000 | 4.5376        |
+| 0.6460 | 39100 | 4.4829        |
+| 0.6476 | 39200 | 4.7344        |
+| 0.6493 | 39300 | 4.4035        |
+| 0.6509 | 39400 | 4.5464        |
+| 0.6526 | 39500 | 4.3932        |
+| 0.6542 | 39600 | 4.3088        |
+| 0.6559 | 39700 | 4.3844        |
+| 0.6575 | 39800 | 4.4635        |
+| 0.6592 | 39900 | 4.205         |
+| 0.6608 | 40000 | 4.5705        |
+| 0.6625 | 40100 | 4.541         |
+| 0.6641 | 40200 | 4.2803        |
+| 0.6658 | 40300 | 4.4778        |
+| 0.6674 | 40400 | 4.3103        |
+| 0.6691 | 40500 | 4.4215        |
+| 0.6707 | 40600 | 4.1347        |
+| 0.6724 | 40700 | 4.4549        |
+| 0.6740 | 40800 | 4.4641        |
+| 0.6757 | 40900 | 4.6036        |
+| 0.6773 | 41000 | 4.1967        |
+| 0.6790 | 41100 | 4.4231        |
+| 0.6806 | 41200 | 4.4425        |
+| 0.6823 | 41300 | 4.5512        |
+| 0.6839 | 41400 | 4.4586        |
+| 0.6856 | 41500 | 4.4396        |
+| 0.6873 | 41600 | 4.281         |
+| 0.6889 | 41700 | 4.4691        |
+| 0.6906 | 41800 | 4.299         |
+| 0.6922 | 41900 | 4.4199        |
+| 0.6939 | 42000 | 4.325         |
+| 0.6955 | 42100 | 4.8069        |
+| 0.6972 | 42200 | 4.4005        |
+| 0.6988 | 42300 | 4.3462        |
+| 0.7005 | 42400 | 4.4979        |
+| 0.7021 | 42500 | 4.3421        |
+| 0.7038 | 42600 | 4.383         |
+| 0.7054 | 42700 | 4.2318        |
+| 0.7071 | 42800 | 4.4444        |
+| 0.7087 | 42900 | 4.3806        |
+| 0.7104 | 43000 | 4.468         |
+| 0.7120 | 43100 | 4.2501        |
+| 0.7137 | 43200 | 4.3727        |
+| 0.7153 | 43300 | 4.388         |
+| 0.7170 | 43400 | 4.3485        |
+| 0.7186 | 43500 | 4.343         |
+| 0.7203 | 43600 | 4.4982        |
+| 0.7219 | 43700 | 4.3745        |
+| 0.7236 | 43800 | 4.4955        |
+| 0.7252 | 43900 | 4.4546        |
+| 0.7269 | 44000 | 4.2144        |
+| 0.7286 | 44100 | 4.5755        |
+| 0.7302 | 44200 | 4.1601        |
+| 0.7319 | 44300 | 4.2967        |
+| 0.7335 | 44400 | 4.4625        |
+| 0.7352 | 44500 | 4.2364        |
+| 0.7368 | 44600 | 4.5778        |
+| 0.7385 | 44700 | 4.2853        |
+| 0.7401 | 44800 | 4.4863        |
+| 0.7418 | 44900 | 4.1957        |
+| 0.7434 | 45000 | 4.2534        |
+| 0.7451 | 45100 | 4.3133        |
+| 0.7467 | 45200 | 4.5476        |
+| 0.7484 | 45300 | 4.3681        |
+| 0.7500 | 45400 | 4.3973        |
+| 0.7517 | 45500 | 4.1377        |
+| 0.7533 | 45600 | 4.2803        |
+| 0.7550 | 45700 | 4.4228        |
+| 0.7566 | 45800 | 4.0531        |
+| 0.7583 | 45900 | 3.9899        |
+| 0.7599 | 46000 | 4.3483        |
+| 0.7616 | 46100 | 4.1261        |
+| 0.7632 | 46200 | 4.5054        |
+| 0.7649 | 46300 | 4.0876        |
+| 0.7665 | 46400 | 4.3376        |
+| 0.7682 | 46500 | 4.1925        |
+| 0.7699 | 46600 | 4.2739        |
+| 0.7715 | 46700 | 4.3682        |
+| 0.7732 | 46800 | 4.441         |
+| 0.7748 | 46900 | 4.4299        |
+| 0.7765 | 47000 | 4.2043        |
+| 0.7781 | 47100 | 4.3618        |
+| 0.7798 | 47200 | 4.1743        |
+| 0.7814 | 47300 | 4.4187        |
+| 0.7831 | 47400 | 4.2229        |
+| 0.7847 | 47500 | 4.3314        |
+| 0.7864 | 47600 | 4.0925        |
+| 0.7880 | 47700 | 4.0808        |
+| 0.7897 | 47800 | 4.5237        |
+| 0.7913 | 47900 | 4.1168        |
+| 0.7930 | 48000 | 4.2941        |
+| 0.7946 | 48100 | 4.384         |
+| 0.7963 | 48200 | 4.7188        |
+| 0.7979 | 48300 | 4.3229        |
+| 0.7996 | 48400 | 4.2011        |
+| 0.8012 | 48500 | 4.2779        |
+| 0.8029 | 48600 | 4.3589        |
+| 0.8045 | 48700 | 4.2659        |
+| 0.8062 | 48800 | 4.5345        |
+| 0.8079 | 48900 | 3.7909        |
+| 0.8095 | 49000 | 4.4958        |
+| 0.8112 | 49100 | 4.1165        |
+| 0.8128 | 49200 | 4.1192        |
+| 0.8145 | 49300 | 4.5164        |
+| 0.8161 | 49400 | 4.0759        |
+| 0.8178 | 49500 | 4.2756        |
+| 0.8194 | 49600 | 4.6745        |
+| 0.8211 | 49700 | 4.2513        |
+| 0.8227 | 49800 | 4.0886        |
+| 0.8244 | 49900 | 4.2688        |
+| 0.8260 | 50000 | 4.2109        |
+| 0.8277 | 50100 | 3.9525        |
+| 0.8293 | 50200 | 4.0889        |
+| 0.8310 | 50300 | 4.1099        |
+| 0.8326 | 50400 | 3.9672        |
+| 0.8343 | 50500 | 4.2584        |
+| 0.8359 | 50600 | 3.9683        |
+| 0.8376 | 50700 | 4.1123        |
+| 0.8392 | 50800 | 4.0991        |
+| 0.8409 | 50900 | 4.2131        |
+| 0.8425 | 51000 | 3.9701        |
+| 0.8442 | 51100 | 4.6632        |
+| 0.8458 | 51200 | 4.5646        |
+| 0.8475 | 51300 | 4.3518        |
+| 0.8492 | 51400 | 4.0883        |
+| 0.8508 | 51500 | 4.5185        |
+| 0.8525 | 51600 | 4.3088        |
+| 0.8541 | 51700 | 4.2788        |
+| 0.8558 | 51800 | 4.4045        |
+| 0.8574 | 51900 | 4.1641        |
+| 0.8591 | 52000 | 4.4632        |
+| 0.8607 | 52100 | 4.1843        |
+| 0.8624 | 52200 | 4.2139        |
+| 0.8640 | 52300 | 4.2557        |
+| 0.8657 | 52400 | 4.0797        |
+| 0.8673 | 52500 | 4.0446        |
+| 0.8690 | 52600 | 4.4987        |
+| 0.8706 | 52700 | 4.1227        |
+| 0.8723 | 52800 | 4.097         |
+| 0.8739 | 52900 | 4.2207        |
+| 0.8756 | 53000 | 4.1675        |
+| 0.8772 | 53100 | 3.964         |
+| 0.8789 | 53200 | 4.3966        |
+| 0.8805 | 53300 | 4.173         |
+| 0.8822 | 53400 | 4.704         |
+| 0.8838 | 53500 | 4.1042        |
+| 0.8855 | 53600 | 3.9662        |
+| 0.8871 | 53700 | 4.315         |
+| 0.8888 | 53800 | 4.295         |
+| 0.8905 | 53900 | 3.997         |
+| 0.8921 | 54000 | 4.4502        |
+| 0.8938 | 54100 | 4.479         |
+| 0.8954 | 54200 | 4.0461        |
+| 0.8971 | 54300 | 4.2015        |
+| 0.8987 | 54400 | 4.3934        |
+| 0.9004 | 54500 | 4.257         |
+| 0.9020 | 54600 | 4.2889        |
+| 0.9037 | 54700 | 4.3432        |
+| 0.9053 | 54800 | 4.2438        |
+| 0.9070 | 54900 | 3.9952        |
+| 0.9086 | 55000 | 4.1644        |
+| 0.9103 | 55100 | 4.2173        |
+| 0.9119 | 55200 | 4.4476        |
+| 0.9136 | 55300 | 4.3303        |
+| 0.9152 | 55400 | 4.2151        |
+| 0.9169 | 55500 | 4.188         |
+| 0.9185 | 55600 | 4.1958        |
+| 0.9202 | 55700 | 4.305         |
+| 0.9218 | 55800 | 3.8768        |
+| 0.9235 | 55900 | 4.2899        |
+| 0.9251 | 56000 | 4.2238        |
+| 0.9268 | 56100 | 4.4298        |
+| 0.9284 | 56200 | 4.325         |
+| 0.9301 | 56300 | 4.5084        |
+| 0.9318 | 56400 | 4.1923        |
+| 0.9334 | 56500 | 4.258         |
+| 0.9351 | 56600 | 3.9049        |
+| 0.9367 | 56700 | 4.1926        |
+| 0.9384 | 56800 | 3.7358        |
+| 0.9400 | 56900 | 4.1174        |
+| 0.9417 | 57000 | 4.0027        |
+| 0.9433 | 57100 | 3.9343        |
+| 0.9450 | 57200 | 4.1863        |
+| 0.9466 | 57300 | 4.0725        |
+| 0.9483 | 57400 | 4.4933        |
+| 0.9499 | 57500 | 3.9865        |
+| 0.9516 | 57600 | 3.9649        |
+| 0.9532 | 57700 | 4.2387        |
+| 0.9549 | 57800 | 4.2372        |
+| 0.9565 | 57900 | 3.9313        |
+| 0.9582 | 58000 | 4.2078        |
+| 0.9598 | 58100 | 4.3646        |
+| 0.9615 | 58200 | 4.0848        |
+| 0.9631 | 58300 | 4.1224        |
+| 0.9648 | 58400 | 4.2916        |
+| 0.9664 | 58500 | 4.0903        |
+| 0.9681 | 58600 | 3.7786        |
+| 0.9698 | 58700 | 4.038         |
+| 0.9714 | 58800 | 4.1145        |
+| 0.9731 | 58900 | 4.0726        |
+| 0.9747 | 59000 | 3.9669        |
+| 0.9764 | 59100 | 4.1096        |
+| 0.9780 | 59200 | 4.2828        |
+| 0.9797 | 59300 | 4.2423        |
+| 0.9813 | 59400 | 4.0985        |
+| 0.9830 | 59500 | 4.6186        |
+| 0.9846 | 59600 | 4.0591        |
+| 0.9863 | 59700 | 3.7101        |
+| 0.9879 | 59800 | 4.1663        |
+| 0.9896 | 59900 | 3.7786        |
+| 0.9912 | 60000 | 4.3359        |
+| 0.9929 | 60100 | 4.1746        |
+| 0.9945 | 60200 | 4.4696        |
+| 0.9962 | 60300 | 4.1991        |
+| 0.9978 | 60400 | 4.2198        |
+| 0.9995 | 60500 | 4.4005        |
+
+</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 sha256:9185548ee2404622ffea6847ea3c99c81c4f3061ff6a332e608643329f50493c
+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]"
+}