Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "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_Dense/config.json

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+{"in_features": 768, "out_features": 768, "bias": true, "activation_function": "torch.nn.modules.activation.Tanh"}

2_Dense/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:83fdc335a8d550880a2f0acfd4ca2c36cd5e9a1df02889936d9fb5c8eafa11e1
+size 2362528

README.md

@@ -0,0 +1,918 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:131157
+- loss:MultipleNegativesRankingLoss
+base_model: sentence-transformers/LaBSE
+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/LaBSE
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE). It maps sentences & paragraphs to a 768-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/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) <!-- at revision 836121a0533e5664b21c7aacc5d22951f2b8b25b -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 768 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': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
+  (3): 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_falabse_onV9e")
+# Run inference
+sentences = [
+    'برای تبدیل شدن به نویسنده برتر Quora ، چند بازدید و پاسخ لازم است؟',
+    'چگونه می توانم نویسند�� برتر Quora شوم ، از صعود بیشتر و آمار 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, 768]
+
+# 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: 6 tokens</li><li>mean: 15.78 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.52 tokens</li><li>max: 57 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`: 12
+- `learning_rate`: 5e-06
+- `weight_decay`: 0.01
+- `num_train_epochs`: 5
+- `warmup_ratio`: 0.1
+- `push_to_hub`: True
+- `hub_model_id`: codersan/validadted_falabse_onV9e
+- `eval_on_start`: True
+- `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`: 12
+- `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`: 5e-06
+- `weight_decay`: 0.01
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 5
+- `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_falabse_onV9e
+- `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`: True
+- `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      | 0     | -             |
+| 0.0091 | 100   | 0.1276        |
+| 0.0183 | 200   | 0.1092        |
+| 0.0274 | 300   | 0.101         |
+| 0.0366 | 400   | 0.0908        |
+| 0.0457 | 500   | 0.0728        |
+| 0.0549 | 600   | 0.0522        |
+| 0.0640 | 700   | 0.0532        |
+| 0.0732 | 800   | 0.0275        |
+| 0.0823 | 900   | 0.0216        |
+| 0.0915 | 1000  | 0.0212        |
+| 0.1006 | 1100  | 0.0318        |
+| 0.1098 | 1200  | 0.0328        |
+| 0.1189 | 1300  | 0.0299        |
+| 0.1281 | 1400  | 0.0412        |
+| 0.1372 | 1500  | 0.0199        |
+| 0.1464 | 1600  | 0.0118        |
+| 0.1555 | 1700  | 0.034         |
+| 0.1647 | 1800  | 0.0282        |
+| 0.1738 | 1900  | 0.027         |
+| 0.1830 | 2000  | 0.0153        |
+| 0.1921 | 2100  | 0.0282        |
+| 0.2013 | 2200  | 0.014         |
+| 0.2104 | 2300  | 0.0221        |
+| 0.2196 | 2400  | 0.0464        |
+| 0.2287 | 2500  | 0.0253        |
+| 0.2379 | 2600  | 0.0176        |
+| 0.2470 | 2700  | 0.0214        |
+| 0.2562 | 2800  | 0.0203        |
+| 0.2653 | 2900  | 0.0273        |
+| 0.2745 | 3000  | 0.0235        |
+| 0.2836 | 3100  | 0.0235        |
+| 0.2928 | 3200  | 0.0202        |
+| 0.3019 | 3300  | 0.014         |
+| 0.3111 | 3400  | 0.0274        |
+| 0.3202 | 3500  | 0.023         |
+| 0.3294 | 3600  | 0.0233        |
+| 0.3385 | 3700  | 0.0211        |
+| 0.3477 | 3800  | 0.0164        |
+| 0.3568 | 3900  | 0.0134        |
+| 0.3660 | 4000  | 0.0152        |
+| 0.3751 | 4100  | 0.0125        |
+| 0.3843 | 4200  | 0.0216        |
+| 0.3934 | 4300  | 0.0148        |
+| 0.4026 | 4400  | 0.0339        |
+| 0.4117 | 4500  | 0.0185        |
+| 0.4209 | 4600  | 0.0226        |
+| 0.4300 | 4700  | 0.0369        |
+| 0.4392 | 4800  | 0.0178        |
+| 0.4483 | 4900  | 0.0125        |
+| 0.4575 | 5000  | 0.0172        |
+| 0.4666 | 5100  | 0.0173        |
+| 0.4758 | 5200  | 0.0098        |
+| 0.4849 | 5300  | 0.0194        |
+| 0.4941 | 5400  | 0.026         |
+| 0.5032 | 5500  | 0.0164        |
+| 0.5124 | 5600  | 0.0317        |
+| 0.5215 | 5700  | 0.016         |
+| 0.5306 | 5800  | 0.024         |
+| 0.5398 | 5900  | 0.0224        |
+| 0.5489 | 6000  | 0.0229        |
+| 0.5581 | 6100  | 0.0124        |
+| 0.5672 | 6200  | 0.0262        |
+| 0.5764 | 6300  | 0.023         |
+| 0.5855 | 6400  | 0.026         |
+| 0.5947 | 6500  | 0.028         |
+| 0.6038 | 6600  | 0.017         |
+| 0.6130 | 6700  | 0.0103        |
+| 0.6221 | 6800  | 0.0137        |
+| 0.6313 | 6900  | 0.0198        |
+| 0.6404 | 7000  | 0.0127        |
+| 0.6496 | 7100  | 0.0125        |
+| 0.6587 | 7200  | 0.0197        |
+| 0.6679 | 7300  | 0.0209        |
+| 0.6770 | 7400  | 0.0208        |
+| 0.6862 | 7500  | 0.0149        |
+| 0.6953 | 7600  | 0.017         |
+| 0.7045 | 7700  | 0.0228        |
+| 0.7136 | 7800  | 0.0161        |
+| 0.7228 | 7900  | 0.015         |
+| 0.7319 | 8000  | 0.0105        |
+| 0.7411 | 8100  | 0.0147        |
+| 0.7502 | 8200  | 0.0131        |
+| 0.7594 | 8300  | 0.0144        |
+| 0.7685 | 8400  | 0.0313        |
+| 0.7777 | 8500  | 0.0118        |
+| 0.7868 | 8600  | 0.0159        |
+| 0.7960 | 8700  | 0.0213        |
+| 0.8051 | 8800  | 0.0273        |
+| 0.8143 | 8900  | 0.0256        |
+| 0.8234 | 9000  | 0.0149        |
+| 0.8326 | 9100  | 0.012         |
+| 0.8417 | 9200  | 0.0294        |
+| 0.8509 | 9300  | 0.0134        |
+| 0.8600 | 9400  | 0.0138        |
+| 0.8692 | 9500  | 0.0127        |
+| 0.8783 | 9600  | 0.0325        |
+| 0.8875 | 9700  | 0.0207        |
+| 0.8966 | 9800  | 0.0174        |
+| 0.9058 | 9900  | 0.0238        |
+| 0.9149 | 10000 | 0.0256        |
+| 0.9241 | 10100 | 0.0197        |
+| 0.9332 | 10200 | 0.0178        |
+| 0.9424 | 10300 | 0.0106        |
+| 0.9515 | 10400 | 0.0224        |
+| 0.9607 | 10500 | 0.0162        |
+| 0.9698 | 10600 | 0.0178        |
+| 0.9790 | 10700 | 0.0244        |
+| 0.9881 | 10800 | 0.0223        |
+| 0.9973 | 10900 | 0.0117        |
+| 1.0064 | 11000 | 0.0261        |
+| 1.0156 | 11100 | 0.02          |
+| 1.0247 | 11200 | 0.0155        |
+| 1.0339 | 11300 | 0.0193        |
+| 1.0430 | 11400 | 0.0312        |
+| 1.0522 | 11500 | 0.0222        |
+| 1.0613 | 11600 | 0.0302        |
+| 1.0704 | 11700 | 0.0126        |
+| 1.0796 | 11800 | 0.0123        |
+| 1.0887 | 11900 | 0.0064        |
+| 1.0979 | 12000 | 0.0083        |
+| 1.1070 | 12100 | 0.0143        |
+| 1.1162 | 12200 | 0.0181        |
+| 1.1253 | 12300 | 0.0311        |
+| 1.1345 | 12400 | 0.0097        |
+| 1.1436 | 12500 | 0.0083        |
+| 1.1528 | 12600 | 0.0125        |
+| 1.1619 | 12700 | 0.0169        |
+| 1.1711 | 12800 | 0.0192        |
+| 1.1802 | 12900 | 0.0086        |
+| 1.1894 | 13000 | 0.0171        |
+| 1.1985 | 13100 | 0.0108        |
+| 1.2077 | 13200 | 0.0079        |
+| 1.2168 | 13300 | 0.0304        |
+| 1.2260 | 13400 | 0.0134        |
+| 1.2351 | 13500 | 0.0124        |
+| 1.2443 | 13600 | 0.0057        |
+| 1.2534 | 13700 | 0.0174        |
+| 1.2626 | 13800 | 0.0195        |
+| 1.2717 | 13900 | 0.0164        |
+| 1.2809 | 14000 | 0.0115        |
+| 1.2900 | 14100 | 0.0152        |
+| 1.2992 | 14200 | 0.004         |
+| 1.3083 | 14300 | 0.0183        |
+| 1.3175 | 14400 | 0.0106        |
+| 1.3266 | 14500 | 0.0196        |
+| 1.3358 | 14600 | 0.006         |
+| 1.3449 | 14700 | 0.0144        |
+| 1.3541 | 14800 | 0.0051        |
+| 1.3632 | 14900 | 0.004         |
+| 1.3724 | 15000 | 0.0091        |
+| 1.3815 | 15100 | 0.0054        |
+| 1.3907 | 15200 | 0.0115        |
+| 1.3998 | 15300 | 0.0156        |
+| 1.4090 | 15400 | 0.0069        |
+| 1.4181 | 15500 | 0.0133        |
+| 1.4273 | 15600 | 0.0177        |
+| 1.4364 | 15700 | 0.0063        |
+| 1.4456 | 15800 | 0.0065        |
+| 1.4547 | 15900 | 0.0101        |
+| 1.4639 | 16000 | 0.0025        |
+| 1.4730 | 16100 | 0.0098        |
+| 1.4822 | 16200 | 0.0058        |
+| 1.4913 | 16300 | 0.0098        |
+| 1.5005 | 16400 | 0.0053        |
+| 1.5096 | 16500 | 0.0052        |
+| 1.5188 | 16600 | 0.0136        |
+| 1.5279 | 16700 | 0.0095        |
+| 1.5371 | 16800 | 0.0111        |
+| 1.5462 | 16900 | 0.0088        |
+| 1.5554 | 17000 | 0.0086        |
+| 1.5645 | 17100 | 0.0098        |
+| 1.5737 | 17200 | 0.0111        |
+| 1.5828 | 17300 | 0.0059        |
+| 1.5919 | 17400 | 0.02          |
+| 1.6011 | 17500 | 0.0102        |
+| 1.6102 | 17600 | 0.004         |
+| 1.6194 | 17700 | 0.0029        |
+| 1.6285 | 17800 | 0.0116        |
+| 1.6377 | 17900 | 0.0031        |
+| 1.6468 | 18000 | 0.0064        |
+| 1.6560 | 18100 | 0.0094        |
+| 1.6651 | 18200 | 0.0121        |
+| 1.6743 | 18300 | 0.0087        |
+| 1.6834 | 18400 | 0.0075        |
+| 1.6926 | 18500 | 0.0052        |
+| 1.7017 | 18600 | 0.0105        |
+| 1.7109 | 18700 | 0.0111        |
+| 1.7200 | 18800 | 0.0074        |
+| 1.7292 | 18900 | 0.0038        |
+| 1.7383 | 19000 | 0.0073        |
+| 1.7475 | 19100 | 0.0042        |
+| 1.7566 | 19200 | 0.0047        |
+| 1.7658 | 19300 | 0.0177        |
+| 1.7749 | 19400 | 0.005         |
+| 1.7841 | 19500 | 0.0062        |
+| 1.7932 | 19600 | 0.0081        |
+| 1.8024 | 19700 | 0.007         |
+| 1.8115 | 19800 | 0.0123        |
+| 1.8207 | 19900 | 0.0076        |
+| 1.8298 | 20000 | 0.006         |
+| 1.8390 | 20100 | 0.0077        |
+| 1.8481 | 20200 | 0.0071        |
+| 1.8573 | 20300 | 0.0054        |
+| 1.8664 | 20400 | 0.0065        |
+| 1.8756 | 20500 | 0.0104        |
+| 1.8847 | 20600 | 0.0099        |
+| 1.8939 | 20700 | 0.0094        |
+| 1.9030 | 20800 | 0.0068        |
+| 1.9122 | 20900 | 0.012         |
+| 1.9213 | 21000 | 0.0098        |
+| 1.9305 | 21100 | 0.0164        |
+| 1.9396 | 21200 | 0.0052        |
+| 1.9488 | 21300 | 0.0131        |
+| 1.9579 | 21400 | 0.0065        |
+| 1.9671 | 21500 | 0.0079        |
+| 1.9762 | 21600 | 0.0042        |
+| 1.9854 | 21700 | 0.0245        |
+| 1.9945 | 21800 | 0.007         |
+| 2.0037 | 21900 | 0.0061        |
+| 2.0128 | 22000 | 0.0087        |
+| 2.0220 | 22100 | 0.0095        |
+| 2.0311 | 22200 | 0.0114        |
+| 2.0403 | 22300 | 0.0178        |
+| 2.0494 | 22400 | 0.0116        |
+| 2.0586 | 22500 | 0.0055        |
+| 2.0677 | 22600 | 0.0142        |
+| 2.0769 | 22700 | 0.0055        |
+| 2.0860 | 22800 | 0.0027        |
+| 2.0952 | 22900 | 0.0036        |
+| 2.1043 | 23000 | 0.0072        |
+| 2.1134 | 23100 | 0.0088        |
+| 2.1226 | 23200 | 0.0125        |
+| 2.1317 | 23300 | 0.0076        |
+| 2.1409 | 23400 | 0.0037        |
+| 2.1500 | 23500 | 0.0034        |
+| 2.1592 | 23600 | 0.0082        |
+| 2.1683 | 23700 | 0.0074        |
+| 2.1775 | 23800 | 0.0118        |
+| 2.1866 | 23900 | 0.0066        |
+| 2.1958 | 24000 | 0.0081        |
+| 2.2049 | 24100 | 0.0031        |
+| 2.2141 | 24200 | 0.0084        |
+| 2.2232 | 24300 | 0.013         |
+| 2.2324 | 24400 | 0.0081        |
+| 2.2415 | 24500 | 0.0034        |
+| 2.2507 | 24600 | 0.0018        |
+| 2.2598 | 24700 | 0.0177        |
+| 2.2690 | 24800 | 0.0075        |
+| 2.2781 | 24900 | 0.0051        |
+| 2.2873 | 25000 | 0.007         |
+| 2.2964 | 25100 | 0.0077        |
+| 2.3056 | 25200 | 0.0038        |
+| 2.3147 | 25300 | 0.0092        |
+| 2.3239 | 25400 | 0.0082        |
+| 2.3330 | 25500 | 0.0039        |
+| 2.3422 | 25600 | 0.0092        |
+| 2.3513 | 25700 | 0.0022        |
+| 2.3605 | 25800 | 0.003         |
+| 2.3696 | 25900 | 0.0038        |
+| 2.3788 | 26000 | 0.0017        |
+| 2.3879 | 26100 | 0.0045        |
+| 2.3971 | 26200 | 0.0069        |
+| 2.4062 | 26300 | 0.003         |
+| 2.4154 | 26400 | 0.0054        |
+| 2.4245 | 26500 | 0.0111        |
+| 2.4337 | 26600 | 0.002         |
+| 2.4428 | 26700 | 0.0023        |
+| 2.4520 | 26800 | 0.0039        |
+| 2.4611 | 26900 | 0.003         |
+| 2.4703 | 27000 | 0.0045        |
+| 2.4794 | 27100 | 0.0007        |
+| 2.4886 | 27200 | 0.0053        |
+| 2.4977 | 27300 | 0.0038        |
+| 2.5069 | 27400 | 0.0023        |
+| 2.5160 | 27500 | 0.0059        |
+| 2.5252 | 27600 | 0.0028        |
+| 2.5343 | 27700 | 0.007         |
+| 2.5435 | 27800 | 0.0052        |
+| 2.5526 | 27900 | 0.006         |
+| 2.5618 | 28000 | 0.0042        |
+| 2.5709 | 28100 | 0.0064        |
+| 2.5801 | 28200 | 0.0025        |
+| 2.5892 | 28300 | 0.0119        |
+| 2.5984 | 28400 | 0.0057        |
+| 2.6075 | 28500 | 0.0053        |
+| 2.6167 | 28600 | 0.0031        |
+| 2.6258 | 28700 | 0.005         |
+| 2.6349 | 28800 | 0.0055        |
+| 2.6441 | 28900 | 0.0018        |
+| 2.6532 | 29000 | 0.0031        |
+| 2.6624 | 29100 | 0.0085        |
+| 2.6715 | 29200 | 0.003         |
+| 2.6807 | 29300 | 0.0043        |
+| 2.6898 | 29400 | 0.0031        |
+| 2.6990 | 29500 | 0.002         |
+| 2.7081 | 29600 | 0.0045        |
+| 2.7173 | 29700 | 0.0086        |
+| 2.7264 | 29800 | 0.0031        |
+| 2.7356 | 29900 | 0.0034        |
+| 2.7447 | 30000 | 0.0032        |
+| 2.7539 | 30100 | 0.0013        |
+| 2.7630 | 30200 | 0.0042        |
+| 2.7722 | 30300 | 0.0043        |
+| 2.7813 | 30400 | 0.0025        |
+| 2.7905 | 30500 | 0.0039        |
+| 2.7996 | 30600 | 0.0038        |
+| 2.8088 | 30700 | 0.0044        |
+| 2.8179 | 30800 | 0.0058        |
+| 2.8271 | 30900 | 0.0016        |
+| 2.8362 | 31000 | 0.0037        |
+| 2.8454 | 31100 | 0.0034        |
+| 2.8545 | 31200 | 0.0044        |
+| 2.8637 | 31300 | 0.0057        |
+| 2.8728 | 31400 | 0.0061        |
+| 2.8820 | 31500 | 0.0082        |
+| 2.8911 | 31600 | 0.0037        |
+| 2.9003 | 31700 | 0.0049        |
+| 2.9094 | 31800 | 0.0058        |
+| 2.9186 | 31900 | 0.0046        |
+| 2.9277 | 32000 | 0.0042        |
+| 2.9369 | 32100 | 0.0087        |
+| 2.9460 | 32200 | 0.0029        |
+| 2.9552 | 32300 | 0.0068        |
+| 2.9643 | 32400 | 0.006         |
+| 2.9735 | 32500 | 0.0037        |
+| 2.9826 | 32600 | 0.0096        |
+| 2.9918 | 32700 | 0.0079        |
+| 3.0009 | 32800 | 0.002         |
+| 3.0101 | 32900 | 0.0049        |
+| 3.0192 | 33000 | 0.0046        |
+| 3.0284 | 33100 | 0.0031        |
+| 3.0375 | 33200 | 0.0091        |
+| 3.0467 | 33300 | 0.0103        |
+| 3.0558 | 33400 | 0.003         |
+| 3.0650 | 33500 | 0.0036        |
+| 3.0741 | 33600 | 0.004         |
+| 3.0833 | 33700 | 0.0024        |
+| 3.0924 | 33800 | 0.0014        |
+| 3.1016 | 33900 | 0.0048        |
+| 3.1107 | 34000 | 0.0044        |
+| 3.1199 | 34100 | 0.0045        |
+| 3.1290 | 34200 | 0.0081        |
+| 3.1382 | 34300 | 0.0014        |
+| 3.1473 | 34400 | 0.0014        |
+| 3.1565 | 34500 | 0.0051        |
+| 3.1656 | 34600 | 0.0029        |
+| 3.1747 | 34700 | 0.0099        |
+| 3.1839 | 34800 | 0.0007        |
+| 3.1930 | 34900 | 0.0074        |
+| 3.2022 | 35000 | 0.0006        |
+| 3.2113 | 35100 | 0.0033        |
+| 3.2205 | 35200 | 0.0054        |
+| 3.2296 | 35300 | 0.0053        |
+| 3.2388 | 35400 | 0.0033        |
+| 3.2479 | 35500 | 0.0009        |
+| 3.2571 | 35600 | 0.0056        |
+| 3.2662 | 35700 | 0.0076        |
+| 3.2754 | 35800 | 0.0018        |
+| 3.2845 | 35900 | 0.0059        |
+| 3.2937 | 36000 | 0.002         |
+| 3.3028 | 36100 | 0.0025        |
+| 3.3120 | 36200 | 0.0044        |
+| 3.3211 | 36300 | 0.0034        |
+| 3.3303 | 36400 | 0.0028        |
+| 3.3394 | 36500 | 0.0031        |
+| 3.3486 | 36600 | 0.0026        |
+| 3.3577 | 36700 | 0.0011        |
+| 3.3669 | 36800 | 0.0007        |
+| 3.3760 | 36900 | 0.0016        |
+| 3.3852 | 37000 | 0.0028        |
+| 3.3943 | 37100 | 0.0013        |
+| 3.4035 | 37200 | 0.0023        |
+| 3.4126 | 37300 | 0.0027        |
+| 3.4218 | 37400 | 0.0037        |
+| 3.4309 | 37500 | 0.005         |
+| 3.4401 | 37600 | 0.0027        |
+| 3.4492 | 37700 | 0.0007        |
+| 3.4584 | 37800 | 0.0041        |
+| 3.4675 | 37900 | 0.0017        |
+| 3.4767 | 38000 | 0.0011        |
+| 3.4858 | 38100 | 0.0021        |
+| 3.4950 | 38200 | 0.0031        |
+| 3.5041 | 38300 | 0.0011        |
+| 3.5133 | 38400 | 0.0035        |
+| 3.5224 | 38500 | 0.0005        |
+| 3.5316 | 38600 | 0.0074        |
+| 3.5407 | 38700 | 0.0017        |
+| 3.5499 | 38800 | 0.0056        |
+| 3.5590 | 38900 | 0.001         |
+| 3.5682 | 39000 | 0.0055        |
+| 3.5773 | 39100 | 0.0021        |
+| 3.5865 | 39200 | 0.0037        |
+| 3.5956 | 39300 | 0.0056        |
+| 3.6048 | 39400 | 0.0044        |
+| 3.6139 | 39500 | 0.0026        |
+| 3.6231 | 39600 | 0.0026        |
+| 3.6322 | 39700 | 0.0033        |
+| 3.6414 | 39800 | 0.0008        |
+| 3.6505 | 39900 | 0.0034        |
+| 3.6597 | 40000 | 0.0029        |
+| 3.6688 | 40100 | 0.0029        |
+| 3.6780 | 40200 | 0.0022        |
+| 3.6871 | 40300 | 0.0032        |
+| 3.6962 | 40400 | 0.0006        |
+| 3.7054 | 40500 | 0.0013        |
+| 3.7145 | 40600 | 0.0084        |
+| 3.7237 | 40700 | 0.0012        |
+| 3.7328 | 40800 | 0.0015        |
+| 3.7420 | 40900 | 0.0015        |
+| 3.7511 | 41000 | 0.0014        |
+| 3.7603 | 41100 | 0.0021        |
+| 3.7694 | 41200 | 0.0015        |
+| 3.7786 | 41300 | 0.0008        |
+| 3.7877 | 41400 | 0.0018        |
+| 3.7969 | 41500 | 0.0019        |
+| 3.8060 | 41600 | 0.0044        |
+| 3.8152 | 41700 | 0.004         |
+| 3.8243 | 41800 | 0.0015        |
+| 3.8335 | 41900 | 0.0023        |
+| 3.8426 | 42000 | 0.0019        |
+| 3.8518 | 42100 | 0.0031        |
+| 3.8609 | 42200 | 0.0032        |
+| 3.8701 | 42300 | 0.0012        |
+| 3.8792 | 42400 | 0.0077        |
+| 3.8884 | 42500 | 0.0052        |
+| 3.8975 | 42600 | 0.0023        |
+| 3.9067 | 42700 | 0.0023        |
+| 3.9158 | 42800 | 0.0034        |
+| 3.9250 | 42900 | 0.0035        |
+| 3.9341 | 43000 | 0.0043        |
+| 3.9433 | 43100 | 0.0018        |
+| 3.9524 | 43200 | 0.003         |
+| 3.9616 | 43300 | 0.0053        |
+| 3.9707 | 43400 | 0.0018        |
+| 3.9799 | 43500 | 0.0051        |
+| 3.9890 | 43600 | 0.004         |
+| 3.9982 | 43700 | 0.001         |
+| 4.0073 | 43800 | 0.0025        |
+| 4.0165 | 43900 | 0.0021        |
+| 4.0256 | 44000 | 0.0028        |
+| 4.0348 | 44100 | 0.0058        |
+| 4.0439 | 44200 | 0.0071        |
+| 4.0531 | 44300 | 0.003         |
+| 4.0622 | 44400 | 0.0018        |
+| 4.0714 | 44500 | 0.0032        |
+| 4.0805 | 44600 | 0.001         |
+| 4.0897 | 44700 | 0.0006        |
+| 4.0988 | 44800 | 0.0017        |
+| 4.1080 | 44900 | 0.0014        |
+| 4.1171 | 45000 | 0.0047        |
+| 4.1263 | 45100 | 0.0031        |
+| 4.1354 | 45200 | 0.001         |
+| 4.1446 | 45300 | 0.0012        |
+| 4.1537 | 45400 | 0.0027        |
+| 4.1629 | 45500 | 0.0015        |
+| 4.1720 | 45600 | 0.0085        |
+| 4.1812 | 45700 | 0.0006        |
+| 4.1903 | 45800 | 0.0027        |
+| 4.1995 | 45900 | 0.0035        |
+| 4.2086 | 46000 | 0.0022        |
+| 4.2177 | 46100 | 0.0029        |
+| 4.2269 | 46200 | 0.0019        |
+| 4.2360 | 46300 | 0.0045        |
+| 4.2452 | 46400 | 0.0005        |
+| 4.2543 | 46500 | 0.0039        |
+| 4.2635 | 46600 | 0.0045        |
+| 4.2726 | 46700 | 0.001         |
+| 4.2818 | 46800 | 0.0028        |
+| 4.2909 | 46900 | 0.0023        |
+| 4.3001 | 47000 | 0.0014        |
+| 4.3092 | 47100 | 0.0017        |
+| 4.3184 | 47200 | 0.0024        |
+| 4.3275 | 47300 | 0.0021        |
+| 4.3367 | 47400 | 0.0017        |
+| 4.3458 | 47500 | 0.0025        |
+| 4.3550 | 47600 | 0.0015        |
+| 4.3641 | 47700 | 0.0004        |
+| 4.3733 | 47800 | 0.0011        |
+| 4.3824 | 47900 | 0.0005        |
+| 4.3916 | 48000 | 0.0028        |
+| 4.4007 | 48100 | 0.0009        |
+| 4.4099 | 48200 | 0.001         |
+| 4.4190 | 48300 | 0.002         |
+| 4.4282 | 48400 | 0.0053        |
+| 4.4373 | 48500 | 0.0008        |
+| 4.4465 | 48600 | 0.0006        |
+| 4.4556 | 48700 | 0.0044        |
+| 4.4648 | 48800 | 0.0005        |
+| 4.4739 | 48900 | 0.0019        |
+| 4.4831 | 49000 | 0.0016        |
+| 4.4922 | 49100 | 0.0018        |
+| 4.5014 | 49200 | 0.0008        |
+| 4.5105 | 49300 | 0.0013        |
+| 4.5197 | 49400 | 0.001         |
+| 4.5288 | 49500 | 0.0046        |
+| 4.5380 | 49600 | 0.0009        |
+| 4.5471 | 49700 | 0.0051        |
+| 4.5563 | 49800 | 0.0017        |
+| 4.5654 | 49900 | 0.0021        |
+| 4.5746 | 50000 | 0.0051        |
+| 4.5837 | 50100 | 0.0014        |
+| 4.5929 | 50200 | 0.0057        |
+| 4.6020 | 50300 | 0.0036        |
+| 4.6112 | 50400 | 0.0027        |
+| 4.6203 | 50500 | 0.0009        |
+| 4.6295 | 50600 | 0.0037        |
+| 4.6386 | 50700 | 0.0004        |
+| 4.6478 | 50800 | 0.0024        |
+| 4.6569 | 50900 | 0.0015        |
+| 4.6661 | 51000 | 0.0026        |
+| 4.6752 | 51100 | 0.0022        |
+| 4.6844 | 51200 | 0.0023        |
+| 4.6935 | 51300 | 0.0007        |
+| 4.7027 | 51400 | 0.0008        |
+| 4.7118 | 51500 | 0.0032        |
+| 4.7210 | 51600 | 0.0031        |
+| 4.7301 | 51700 | 0.0014        |
+| 4.7392 | 51800 | 0.0014        |
+| 4.7484 | 51900 | 0.001         |
+| 4.7575 | 52000 | 0.0011        |
+| 4.7667 | 52100 | 0.0009        |
+| 4.7758 | 52200 | 0.0007        |
+| 4.7850 | 52300 | 0.0026        |
+| 4.7941 | 52400 | 0.0008        |
+| 4.8033 | 52500 | 0.0028        |
+| 4.8124 | 52600 | 0.0019        |
+| 4.8216 | 52700 | 0.0016        |
+| 4.8307 | 52800 | 0.002         |
+| 4.8399 | 52900 | 0.0008        |
+| 4.8490 | 53000 | 0.0025        |
+| 4.8582 | 53100 | 0.0008        |
+| 4.8673 | 53200 | 0.0025        |
+| 4.8765 | 53300 | 0.0039        |
+| 4.8856 | 53400 | 0.0079        |
+| 4.8948 | 53500 | 0.0016        |
+| 4.9039 | 53600 | 0.0014        |
+| 4.9131 | 53700 | 0.0018        |
+| 4.9222 | 53800 | 0.002         |
+| 4.9314 | 53900 | 0.0049        |
+| 4.9405 | 54000 | 0.0012        |
+| 4.9497 | 54100 | 0.0033        |
+| 4.9588 | 54200 | 0.0027        |
+| 4.9680 | 54300 | 0.004         |
+| 4.9771 | 54400 | 0.0011        |
+| 4.9863 | 54500 | 0.006         |
+| 4.9954 | 54600 | 0.0017        |
+
+</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 sha256:7ebb2390f78003100b7e4544ec51cc6c15a7a46857ba11206fcc63b62a9ad4c3
+oid sha256:574ef6ba1af95789c690e1306f2e6c17cd27b99e1025efb3f9eefc206d7d08a2
 size 1883730160

modules.json

@@ -0,0 +1,26 @@
+[
+  {
+    "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_Dense",
+    "type": "sentence_transformers.models.Dense"
+  },
+  {
+    "idx": 3,
+    "name": "3",
+    "path": "3_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 256,
+  "do_lower_case": false
+}