Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,676 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:131157
+- loss:MultipleNegativesRankingLoss
+base_model: sentence-transformers/all-MiniLM-L6-v2
+widget:
+- source_sentence: عواقب ممنوعیت یادداشت های 500 روپیه و 1000 روپیه در مورد اقتصاد
+    هند چیست؟
+  sentences:
+  - آیا باید در فیزیک و علوم کامپیوتر دو برابر کنم؟
+  - چگونه اقتصاد هند پس از ممنوعیت 500 1000 یادداشت تحت تأثیر قرار گرفت؟
+  - آیا آلمان در اجازه پناهندگان سوری به کشور خود اشتباه کرد؟
+- source_sentence: بهترین شماره پشتیبانی فنی QuickBooks در نیویورک ، ایالات متحده
+    کدام است؟
+  sentences:
+  - فناوری هایی که اکثر مردم از آنها نمی دانند چیست؟
+  - بهترین شماره پشتیبانی QuickBooks در آرکانزاس چیست؟
+  - چرا در مقایسه با طرف نزدیک ، دهانه های زیادی در قسمت دور ماه وجود دارد؟
+- source_sentence: اقدامات احتیاطی ایمنی در مورد استفاده از اسلحه های پیشنهادی NRA
+    در میشیگان چیست؟
+  sentences:
+  - پیروزی ترامپ چگونه بر کانادا تأثیر خواهد گذاشت؟
+  - اقدامات احتیاطی ایمنی در مورد استفاده از اسلحه های پیشنهادی NRA در آیداهو چیست؟
+  - مزایای خرید بیمه عمر چیست؟
+- source_sentence: چرا این همه افراد ناراضی هستند؟
+  sentences:
+  - چرا آب نبات تافی آب شور در مغولستان وارد می شود؟
+  - برای یک رابطه موفق از راه دور چه چیزی طول می کشد؟
+  - چرا مردم ناراضی هستند؟
+- source_sentence: برای تبدیل شدن به نویسنده برتر Quora ، چند بازدید و پاسخ لازم است؟
+  sentences:
+  - چگونه می توانم نویسنده برتر Quora شوم ، از صعود بیشتر و آمار بهتر استفاده کنم؟
+  - چرا بسیاری از افرادی که سؤالاتی را در Quora ارسال می کنند ، ابتدا Google را بررسی
+    می کنند؟
+  - من به دنبال خرید دوچرخه جدید هستم.Suzuki Gixxer 155 یا Honda Hornet 160r.کدام
+    یک را بخرید؟
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
+
+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 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 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("codersan/validadted_all-MiniLM_onV9")
+# Run inference
+sentences = [
+    'برای تبدیل شدن به نویسنده برتر Quora ، چند بازدید و پاسخ لازم است؟',
+    'چگونه می توانم نویسنده برتر Quora شوم ، از صعود بیشتر و آما�� بهتر استفاده کنم؟',
+    'من به دنبال خرید دوچرخه جدید هستم.Suzuki Gixxer 155 یا Honda Hornet 160r.کدام یک را بخرید؟',
+]
+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 Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 131,157 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                              | positive                                                                           |
+  |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                              | string                                                                             |
+  | details | <ul><li>min: 11 tokens</li><li>mean: 44.91 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 44.6 tokens</li><li>max: 154 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                        | positive                                                                                                                                            |
+  |:----------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>وقتی سوال من به عنوان "این سوال ممکن است به ویرایش نیاز داشته باشد" چه کاری باید انجام دهم ، اما نمی توانم دلیل آن را پیدا کنم؟</code>  | <code>چرا سوال من به عنوان نیاز به پیشرفت مشخص شده است؟</code>                                                                                      |
+  | <code>چگونه می توانید یک فایل رمزگذاری شده را با دانستن اینکه این یک فایل تصویری است بدون دانستن گسترش پرونده یا کلید ، رمزگشایی کنید؟</code> | <code>چگونه می توانید یک فایل رمزگذاری شده را رمزگشایی کنید و بدانید که این یک فایل تصویری است بدون اینکه از پسوند پرونده اطلاع داشته باشید؟</code> |
+  | <code>احساس می کنم خودکشی می کنم ، چگونه باید با آن برخورد کنم؟</code>                                                                        | <code>احساس می کنم خودکشی می کنم.چه کاری باید انجام دهم؟</code>                                                                                     |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 64
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.01
+- `num_train_epochs`: 15
+- `warmup_ratio`: 0.1
+- `push_to_hub`: True
+- `hub_model_id`: codersan/validadted_all-MiniLM_onV9
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 8
+- `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`: 0.01
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 15
+- `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`: False
+- `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`: True
+- `resume_from_checkpoint`: None
+- `hub_model_id`: codersan/validadted_all-MiniLM_onV9
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch   | Step  | Training Loss |
+|:-------:|:-----:|:-------------:|
+| 0.0488  | 100   | 2.841         |
+| 0.0976  | 200   | 2.1716        |
+| 0.1463  | 300   | 1.5024        |
+| 0.1951  | 400   | 1.2579        |
+| 0.2439  | 500   | 1.1434        |
+| 0.2927  | 600   | 1.0665        |
+| 0.3415  | 700   | 0.9581        |
+| 0.3902  | 800   | 0.9106        |
+| 0.4390  | 900   | 0.87          |
+| 0.4878  | 1000  | 0.7785        |
+| 0.5366  | 1100  | 0.7591        |
+| 0.5854  | 1200  | 0.6928        |
+| 0.6341  | 1300  | 0.6778        |
+| 0.6829  | 1400  | 0.6395        |
+| 0.7317  | 1500  | 0.6145        |
+| 0.7805  | 1600  | 0.5678        |
+| 0.8293  | 1700  | 0.5602        |
+| 0.8780  | 1800  | 0.5498        |
+| 0.9268  | 1900  | 0.5292        |
+| 0.9756  | 2000  | 0.4819        |
+| 1.0244  | 2100  | 0.4717        |
+| 1.0732  | 2200  | 0.4837        |
+| 1.1220  | 2300  | 0.4404        |
+| 1.1707  | 2400  | 0.4359        |
+| 1.2195  | 2500  | 0.4121        |
+| 1.2683  | 2600  | 0.434         |
+| 1.3171  | 2700  | 0.4018        |
+| 1.3659  | 2800  | 0.3866        |
+| 1.4146  | 2900  | 0.3889        |
+| 1.4634  | 3000  | 0.3595        |
+| 1.5122  | 3100  | 0.3547        |
+| 1.5610  | 3200  | 0.3517        |
+| 1.6098  | 3300  | 0.3331        |
+| 1.6585  | 3400  | 0.3228        |
+| 1.7073  | 3500  | 0.3101        |
+| 1.7561  | 3600  | 0.3071        |
+| 1.8049  | 3700  | 0.288         |
+| 1.8537  | 3800  | 0.3115        |
+| 1.9024  | 3900  | 0.2777        |
+| 1.9512  | 4000  | 0.2902        |
+| 2.0     | 4100  | 0.2926        |
+| 2.0488  | 4200  | 0.2958        |
+| 2.0976  | 4300  | 0.2688        |
+| 2.1463  | 4400  | 0.2647        |
+| 2.1951  | 4500  | 0.2523        |
+| 2.2439  | 4600  | 0.2681        |
+| 2.2927  | 4700  | 0.2714        |
+| 2.3415  | 4800  | 0.2575        |
+| 2.3902  | 4900  | 0.2462        |
+| 2.4390  | 5000  | 0.2466        |
+| 2.4878  | 5100  | 0.2215        |
+| 2.5366  | 5200  | 0.2424        |
+| 2.5854  | 5300  | 0.2264        |
+| 2.6341  | 5400  | 0.2252        |
+| 2.6829  | 5500  | 0.2228        |
+| 2.7317  | 5600  | 0.2337        |
+| 2.7805  | 5700  | 0.1983        |
+| 2.8293  | 5800  | 0.2156        |
+| 2.8780  | 5900  | 0.2088        |
+| 2.9268  | 6000  | 0.2196        |
+| 2.9756  | 6100  | 0.2054        |
+| 3.0244  | 6200  | 0.2114        |
+| 3.0732  | 6300  | 0.2191        |
+| 3.1220  | 6400  | 0.1899        |
+| 3.1707  | 6500  | 0.1958        |
+| 3.2195  | 6600  | 0.1907        |
+| 3.2683  | 6700  | 0.2151        |
+| 3.3171  | 6800  | 0.1918        |
+| 3.3659  | 6900  | 0.1859        |
+| 3.4146  | 7000  | 0.1962        |
+| 3.4634  | 7100  | 0.1807        |
+| 3.5122  | 7200  | 0.1874        |
+| 3.5610  | 7300  | 0.179         |
+| 3.6098  | 7400  | 0.1779        |
+| 3.6585  | 7500  | 0.1726        |
+| 3.7073  | 7600  | 0.1693        |
+| 3.7561  | 7700  | 0.1708        |
+| 3.8049  | 7800  | 0.1697        |
+| 3.8537  | 7900  | 0.1744        |
+| 3.9024  | 8000  | 0.1581        |
+| 3.9512  | 8100  | 0.1761        |
+| 4.0     | 8200  | 0.1724        |
+| 4.0488  | 8300  | 0.1777        |
+| 4.0976  | 8400  | 0.1591        |
+| 4.1463  | 8500  | 0.1559        |
+| 4.1951  | 8600  | 0.1518        |
+| 4.2439  | 8700  | 0.1608        |
+| 4.2927  | 8800  | 0.1751        |
+| 4.3415  | 8900  | 0.1572        |
+| 4.3902  | 9000  | 0.1498        |
+| 4.4390  | 9100  | 0.16          |
+| 4.4878  | 9200  | 0.137         |
+| 4.5366  | 9300  | 0.1545        |
+| 4.5854  | 9400  | 0.1443        |
+| 4.6341  | 9500  | 0.1482        |
+| 4.6829  | 9600  | 0.1383        |
+| 4.7317  | 9700  | 0.1468        |
+| 4.7805  | 9800  | 0.1331        |
+| 4.8293  | 9900  | 0.1471        |
+| 4.8780  | 10000 | 0.1352        |
+| 4.9268  | 10100 | 0.1474        |
+| 4.9756  | 10200 | 0.1465        |
+| 5.0244  | 10300 | 0.1401        |
+| 5.0732  | 10400 | 0.1488        |
+| 5.1220  | 10500 | 0.1285        |
+| 5.1707  | 10600 | 0.1326        |
+| 5.2195  | 10700 | 0.1246        |
+| 5.2683  | 10800 | 0.1532        |
+| 5.3171  | 10900 | 0.1345        |
+| 5.3659  | 11000 | 0.1246        |
+| 5.4146  | 11100 | 0.1344        |
+| 5.4634  | 11200 | 0.1214        |
+| 5.5122  | 11300 | 0.1283        |
+| 5.5610  | 11400 | 0.1235        |
+| 5.6098  | 11500 | 0.1265        |
+| 5.6585  | 11600 | 0.1248        |
+| 5.7073  | 11700 | 0.1204        |
+| 5.7561  | 11800 | 0.119         |
+| 5.8049  | 11900 | 0.1174        |
+| 5.8537  | 12000 | 0.1273        |
+| 5.9024  | 12100 | 0.1107        |
+| 5.9512  | 12200 | 0.1277        |
+| 6.0     | 12300 | 0.1178        |
+| 6.0488  | 12400 | 0.1286        |
+| 6.0976  | 12500 | 0.1145        |
+| 6.1463  | 12600 | 0.1164        |
+| 6.1951  | 12700 | 0.1134        |
+| 6.2439  | 12800 | 0.1211        |
+| 6.2927  | 12900 | 0.125         |
+| 6.3415  | 13000 | 0.1187        |
+| 6.3902  | 13100 | 0.1108        |
+| 6.4390  | 13200 | 0.1148        |
+| 6.4878  | 13300 | 0.1046        |
+| 6.5366  | 13400 | 0.1097        |
+| 6.5854  | 13500 | 0.1066        |
+| 6.6341  | 13600 | 0.1078        |
+| 6.6829  | 13700 | 0.102         |
+| 6.7317  | 13800 | 0.107         |
+| 6.7805  | 13900 | 0.1008        |
+| 6.8293  | 14000 | 0.1113        |
+| 6.8780  | 14100 | 0.0987        |
+| 6.9268  | 14200 | 0.1123        |
+| 6.9756  | 14300 | 0.1062        |
+| 7.0244  | 14400 | 0.1101        |
+| 7.0732  | 14500 | 0.1129        |
+| 7.1220  | 14600 | 0.0963        |
+| 7.1707  | 14700 | 0.1053        |
+| 7.2195  | 14800 | 0.0988        |
+| 7.2683  | 14900 | 0.119         |
+| 7.3171  | 15000 | 0.0993        |
+| 7.3659  | 15100 | 0.0986        |
+| 7.4146  | 15200 | 0.1012        |
+| 7.4634  | 15300 | 0.0902        |
+| 7.5122  | 15400 | 0.103         |
+| 7.5610  | 15500 | 0.0961        |
+| 7.6098  | 15600 | 0.0981        |
+| 7.6585  | 15700 | 0.0972        |
+| 7.7073  | 15800 | 0.0965        |
+| 7.7561  | 15900 | 0.0916        |
+| 7.8049  | 16000 | 0.0943        |
+| 7.8537  | 16100 | 0.0973        |
+| 7.9024  | 16200 | 0.0828        |
+| 7.9512  | 16300 | 0.1036        |
+| 8.0     | 16400 | 0.0986        |
+| 8.0488  | 16500 | 0.1008        |
+| 8.0976  | 16600 | 0.0897        |
+| 8.1463  | 16700 | 0.092         |
+| 8.1951  | 16800 | 0.0901        |
+| 8.2439  | 16900 | 0.0979        |
+| 8.2927  | 17000 | 0.0989        |
+| 8.3415  | 17100 | 0.0937        |
+| 8.3902  | 17200 | 0.0882        |
+| 8.4390  | 17300 | 0.0902        |
+| 8.4878  | 17400 | 0.0792        |
+| 8.5366  | 17500 | 0.0893        |
+| 8.5854  | 17600 | 0.0861        |
+| 8.6341  | 17700 | 0.0866        |
+| 8.6829  | 17800 | 0.0831        |
+| 8.7317  | 17900 | 0.0893        |
+| 8.7805  | 18000 | 0.0785        |
+| 8.8293  | 18100 | 0.093         |
+| 8.8780  | 18200 | 0.0815        |
+| 8.9268  | 18300 | 0.0929        |
+| 8.9756  | 18400 | 0.0869        |
+| 9.0244  | 18500 | 0.0874        |
+| 9.0732  | 18600 | 0.0944        |
+| 9.1220  | 18700 | 0.0809        |
+| 9.1707  | 18800 | 0.0845        |
+| 9.2195  | 18900 | 0.0812        |
+| 9.2683  | 19000 | 0.0966        |
+| 9.3171  | 19100 | 0.0819        |
+| 9.3659  | 19200 | 0.08          |
+| 9.4146  | 19300 | 0.0849        |
+| 9.4634  | 19400 | 0.0773        |
+| 9.5122  | 19500 | 0.0822        |
+| 9.5610  | 19600 | 0.0781        |
+| 9.6098  | 19700 | 0.0798        |
+| 9.6585  | 19800 | 0.0745        |
+| 9.7073  | 19900 | 0.0763        |
+| 9.7561  | 20000 | 0.074         |
+| 9.8049  | 20100 | 0.0786        |
+| 9.8537  | 20200 | 0.082         |
+| 9.9024  | 20300 | 0.0685        |
+| 9.9512  | 20400 | 0.0857        |
+| 10.0    | 20500 | 0.0791        |
+| 10.0488 | 20600 | 0.0865        |
+| 10.0976 | 20700 | 0.0801        |
+| 10.1463 | 20800 | 0.0792        |
+| 10.1951 | 20900 | 0.0754        |
+| 10.2439 | 21000 | 0.082         |
+| 10.2927 | 21100 | 0.0849        |
+| 10.3415 | 21200 | 0.0765        |
+| 10.3902 | 21300 | 0.0749        |
+| 10.4390 | 21400 | 0.0793        |
+| 10.4878 | 21500 | 0.0702        |
+| 10.5366 | 21600 | 0.0751        |
+| 10.5854 | 21700 | 0.074         |
+| 10.6341 | 21800 | 0.0733        |
+| 10.6829 | 21900 | 0.0743        |
+| 10.7317 | 22000 | 0.0747        |
+| 10.7805 | 22100 | 0.0658        |
+| 10.8293 | 22200 | 0.0787        |
+| 10.8780 | 22300 | 0.07          |
+| 10.9268 | 22400 | 0.0803        |
+| 10.9756 | 22500 | 0.074         |
+| 11.0244 | 22600 | 0.0737        |
+| 11.0732 | 22700 | 0.0769        |
+| 11.1220 | 22800 | 0.0652        |
+| 11.1707 | 22900 | 0.0714        |
+| 11.2195 | 23000 | 0.0682        |
+| 11.2683 | 23100 | 0.0873        |
+| 11.3171 | 23200 | 0.0693        |
+| 11.3659 | 23300 | 0.069         |
+| 11.4146 | 23400 | 0.0747        |
+| 11.4634 | 23500 | 0.0647        |
+| 11.5122 | 23600 | 0.0737        |
+| 11.5610 | 23700 | 0.0714        |
+| 11.6098 | 23800 | 0.0715        |
+| 11.6585 | 23900 | 0.0666        |
+| 11.7073 | 24000 | 0.0702        |
+| 11.7561 | 24100 | 0.0643        |
+| 11.8049 | 24200 | 0.0654        |
+| 11.8537 | 24300 | 0.0685        |
+| 11.9024 | 24400 | 0.0593        |
+| 11.9512 | 24500 | 0.0775        |
+| 12.0    | 24600 | 0.0721        |
+| 12.0488 | 24700 | 0.076         |
+| 12.0976 | 24800 | 0.0653        |
+| 12.1463 | 24900 | 0.0677        |
+| 12.1951 | 25000 | 0.0652        |
+| 12.2439 | 25100 | 0.076         |
+| 12.2927 | 25200 | 0.0741        |
+| 12.3415 | 25300 | 0.0677        |
+| 12.3902 | 25400 | 0.065         |
+| 12.4390 | 25500 | 0.0709        |
+| 12.4878 | 25600 | 0.0625        |
+| 12.5366 | 25700 | 0.0666        |
+| 12.5854 | 25800 | 0.0665        |
+| 12.6341 | 25900 | 0.0679        |
+| 12.6829 | 26000 | 0.0636        |
+| 12.7317 | 26100 | 0.0638        |
+| 12.7805 | 26200 | 0.0596        |
+| 12.8293 | 26300 | 0.0693        |
+| 12.8780 | 26400 | 0.0588        |
+| 12.9268 | 26500 | 0.0726        |
+| 12.9756 | 26600 | 0.0671        |
+| 13.0244 | 26700 | 0.0666        |
+| 13.0732 | 26800 | 0.0711        |
+| 13.1220 | 26900 | 0.0604        |
+| 13.1707 | 27000 | 0.0687        |
+| 13.2195 | 27100 | 0.0613        |
+| 13.2683 | 27200 | 0.0781        |
+| 13.3171 | 27300 | 0.0596        |
+| 13.3659 | 27400 | 0.0627        |
+| 13.4146 | 27500 | 0.0655        |
+| 13.4634 | 27600 | 0.0589        |
+| 13.5122 | 27700 | 0.0633        |
+| 13.5610 | 27800 | 0.0622        |
+| 13.6098 | 27900 | 0.065         |
+| 13.6585 | 28000 | 0.06          |
+| 13.7073 | 28100 | 0.063         |
+| 13.7561 | 28200 | 0.0589        |
+| 13.8049 | 28300 | 0.0623        |
+| 13.8537 | 28400 | 0.062         |
+| 13.9024 | 28500 | 0.0559        |
+| 13.9512 | 28600 | 0.0723        |
+| 14.0    | 28700 | 0.0658        |
+| 14.0488 | 28800 | 0.0687        |
+| 14.0976 | 28900 | 0.0606        |
+| 14.1463 | 29000 | 0.0622        |
+| 14.1951 | 29100 | 0.0604        |
+| 14.2439 | 29200 | 0.0657        |
+| 14.2927 | 29300 | 0.067         |
+| 14.3415 | 29400 | 0.0653        |
+| 14.3902 | 29500 | 0.0587        |
+| 14.4390 | 29600 | 0.0641        |
+| 14.4878 | 29700 | 0.0558        |
+| 14.5366 | 29800 | 0.0625        |
+| 14.5854 | 29900 | 0.0613        |
+| 14.6341 | 30000 | 0.0618        |
+| 14.6829 | 30100 | 0.0596        |
+| 14.7317 | 30200 | 0.0575        |
+| 14.7805 | 30300 | 0.0552        |
+| 14.8293 | 30400 | 0.0669        |
+| 14.8780 | 30500 | 0.0552        |
+| 14.9268 | 30600 | 0.0665        |
+| 14.9756 | 30700 | 0.0625        |
+
+</details>
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.3.1
+- Transformers: 4.47.0
+- PyTorch: 2.5.1+cu121
+- Accelerate: 1.2.1
+- Datasets: 3.2.0
+- Tokenizers: 0.21.0
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## 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_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.47.0",
+    "pytorch": "2.5.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5c6fdd632d4bac9eb0e53bdef1f0925d764e303a75f7154192659cf9cd9e24c5
+oid sha256:f557679d4df3c3881eb82928e3d65d631b3dea37b6ce76901b91daedf490c5d2
 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
+}