Add new SentenceTransformer model

1_Pooling/config.json

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+{
+  "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 sha256:13fd3dcf128ef15f358306ee7333f2c77a056d9713520a3d203c743fb076bc90
+size 2362528

README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:172826
+- loss:CosineSimilarityLoss
+base_model: sentence-transformers/LaBSE
+widget:
+- source_sentence: How do you make Yahoo your homepage?
+  sentences:
+  - چگونه ویکی پدیا بدون تبلیغ در وب سایت خود درآمد کسب می کند؟
+  - چگونه می توانم برای امتحان INS 21 آماده شوم؟
+  - How can I make Yahoo my homepage on my browser?
+- source_sentence: کدام VPN رایگان در چین کار می کند؟
+  sentences:
+  - VPN های رایگان که در چین کار می کنند چیست؟
+  - How can I stop masturbations?
+  - آیا مدرسه خلاقیت را می کشد؟
+- source_sentence: چند روش خوب برای کاهش وزن چیست؟
+  sentences:
+  - چگونه می توانم یک کتاب خوب بنویسم؟
+  - من اضافه وزن دارمچگونه می توانم وزن کم کنم؟
+  - آیا می توانید ببینید چه کسی داستانهای اینستاگرام شما را مشاهده می کند؟
+- source_sentence: چگونه می توان یک Dell Inspiron 1525 را به تنظیمات کارخانه بازگرداند؟
+  sentences:
+  - چگونه می توان یک Dell Inspiron B130 را به تنظیمات کارخانه بازگرداند؟
+  - مبدل چیست؟
+  - چگونه زندگی شما بعد از تشخیص HIV مثبت تغییر کرد؟
+- source_sentence: داشتن هزاران دنبال کننده در Quora چگونه است؟
+  sentences:
+  - چگونه Airprint HP OfficeJet 4620 با HP LaserJet Enterprise M606X مقایسه می شود؟
+  - چه چیزی است که ده ها هزار دنبال کننده در Quora داشته باشید؟
+  - اگر هند واردات همه محصولات چینی را ممنوع کند ، چه می شود؟
+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_withCosSimb")
+# Run inference
+sentences = [
+    'داشتن هزاران دنبال کننده در Quora چگونه است؟',
+    'چه چیزی است که ده ها هزار دنبال کننده در Quora داشته باشید؟',
+    'چگونه Airprint HP OfficeJet 4620 با HP LaserJet Enterprise M606X مقایسه می شود؟',
+]
+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: 172,826 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                        | sentence2                                                                         | score                                                           |
+  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                            | float                                                           |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 15.2 tokens</li><li>max: 80 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.47 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 0.76</li><li>mean: 0.95</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                          | sentence2                                                      | score                           |
+  |:-------------------------------------------------------------------|:---------------------------------------------------------------|:--------------------------------|
+  | <code>تفاوت بین تحلیلگر تحقیقات بازار و تحلیلگر تجارت چیست؟</code> | <code>تفاوت بین تحقیقات بازاریابی و تحلیلگر تجارت چیست؟</code> | <code>0.982593297958374</code>  |
+  | <code>خوردن چه چیزی باعث دل درد میشود؟</code>                      | <code>چه چیزی باعث رفع دل درد میشود؟</code>                    | <code>0.9582258462905884</code> |
+  | <code>بهترین نرم افزار ویرایش ویدیویی کدام است؟</code>             | <code>بهترین نرم افزار برای ویرایش ویدیو چیست؟</code>          | <code>0.9890836477279663</code> |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
+  ```json
+  {
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 12
+- `learning_rate`: 1e-05
+- `weight_decay`: 0.01
+- `num_train_epochs`: 5
+- `warmup_ratio`: 0.1
+- `push_to_hub`: True
+- `hub_model_id`: codersan/validadted_faLabse_withCosSimb
+- `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`: 1e-05
+- `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_withCosSimb
+- `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     | -             |
+| 0.0069 | 100   | 0.0313        |
+| 0.0139 | 200   | 0.0264        |
+| 0.0208 | 300   | 0.0163        |
+| 0.0278 | 400   | 0.0092        |
+| 0.0347 | 500   | 0.0044        |
+| 0.0417 | 600   | 0.0018        |
+| 0.0486 | 700   | 0.0011        |
+| 0.0555 | 800   | 0.0007        |
+| 0.0625 | 900   | 0.0006        |
+| 0.0694 | 1000  | 0.0006        |
+| 0.0764 | 1100  | 0.0006        |
+| 0.0833 | 1200  | 0.0005        |
+| 0.0903 | 1300  | 0.0005        |
+| 0.0972 | 1400  | 0.0005        |
+| 0.1041 | 1500  | 0.0005        |
+| 0.1111 | 1600  | 0.0005        |
+| 0.1180 | 1700  | 0.0004        |
+| 0.1250 | 1800  | 0.0004        |
+| 0.1319 | 1900  | 0.0004        |
+| 0.1389 | 2000  | 0.0004        |
+| 0.1458 | 2100  | 0.0004        |
+| 0.1527 | 2200  | 0.0004        |
+| 0.1597 | 2300  | 0.0004        |
+| 0.1666 | 2400  | 0.0004        |
+| 0.1736 | 2500  | 0.0003        |
+| 0.1805 | 2600  | 0.0004        |
+| 0.1875 | 2700  | 0.0003        |
+| 0.1944 | 2800  | 0.0003        |
+| 0.2013 | 2900  | 0.0003        |
+| 0.2083 | 3000  | 0.0003        |
+| 0.2152 | 3100  | 0.0003        |
+| 0.2222 | 3200  | 0.0003        |
+| 0.2291 | 3300  | 0.0003        |
+| 0.2361 | 3400  | 0.0003        |
+| 0.2430 | 3500  | 0.0003        |
+| 0.2499 | 3600  | 0.0003        |
+| 0.2569 | 3700  | 0.0003        |
+| 0.2638 | 3800  | 0.0003        |
+| 0.2708 | 3900  | 0.0003        |
+| 0.2777 | 4000  | 0.0003        |
+| 0.2847 | 4100  | 0.0003        |
+| 0.2916 | 4200  | 0.0003        |
+| 0.2985 | 4300  | 0.0003        |
+| 0.3055 | 4400  | 0.0003        |
+| 0.3124 | 4500  | 0.0002        |
+| 0.3194 | 4600  | 0.0002        |
+| 0.3263 | 4700  | 0.0002        |
+| 0.3333 | 4800  | 0.0003        |
+| 0.3402 | 4900  | 0.0002        |
+| 0.3471 | 5000  | 0.0002        |
+| 0.3541 | 5100  | 0.0002        |
+| 0.3610 | 5200  | 0.0002        |
+| 0.3680 | 5300  | 0.0002        |
+| 0.3749 | 5400  | 0.0002        |
+| 0.3819 | 5500  | 0.0002        |
+| 0.3888 | 5600  | 0.0002        |
+| 0.3958 | 5700  | 0.0002        |
+| 0.4027 | 5800  | 0.0002        |
+| 0.4096 | 5900  | 0.0002        |
+| 0.4166 | 6000  | 0.0002        |
+| 0.4235 | 6100  | 0.0002        |
+| 0.4305 | 6200  | 0.0002        |
+| 0.4374 | 6300  | 0.0002        |
+| 0.4444 | 6400  | 0.0002        |
+| 0.4513 | 6500  | 0.0002        |
+| 0.4582 | 6600  | 0.0002        |
+| 0.4652 | 6700  | 0.0002        |
+| 0.4721 | 6800  | 0.0002        |
+| 0.4791 | 6900  | 0.0002        |
+| 0.4860 | 7000  | 0.0002        |
+| 0.4930 | 7100  | 0.0002        |
+| 0.4999 | 7200  | 0.0002        |
+| 0.5068 | 7300  | 0.0002        |
+| 0.5138 | 7400  | 0.0002        |
+| 0.5207 | 7500  | 0.0002        |
+| 0.5277 | 7600  | 0.0002        |
+| 0.5346 | 7700  | 0.0002        |
+| 0.5416 | 7800  | 0.0002        |
+| 0.5485 | 7900  | 0.0002        |
+| 0.5554 | 8000  | 0.0002        |
+| 0.5624 | 8100  | 0.0002        |
+| 0.5693 | 8200  | 0.0002        |
+| 0.5763 | 8300  | 0.0002        |
+| 0.5832 | 8400  | 0.0002        |
+| 0.5902 | 8500  | 0.0002        |
+| 0.5971 | 8600  | 0.0002        |
+| 0.6040 | 8700  | 0.0002        |
+| 0.6110 | 8800  | 0.0002        |
+| 0.6179 | 8900  | 0.0002        |
+| 0.6249 | 9000  | 0.0002        |
+| 0.6318 | 9100  | 0.0002        |
+| 0.6388 | 9200  | 0.0002        |
+| 0.6457 | 9300  | 0.0002        |
+| 0.6526 | 9400  | 0.0002        |
+| 0.6596 | 9500  | 0.0002        |
+| 0.6665 | 9600  | 0.0002        |
+| 0.6735 | 9700  | 0.0002        |
+| 0.6804 | 9800  | 0.0002        |
+| 0.6874 | 9900  | 0.0002        |
+| 0.6943 | 10000 | 0.0002        |
+| 0.7012 | 10100 | 0.0002        |
+| 0.7082 | 10200 | 0.0002        |
+| 0.7151 | 10300 | 0.0002        |
+| 0.7221 | 10400 | 0.0002        |
+| 0.7290 | 10500 | 0.0002        |
+| 0.7360 | 10600 | 0.0002        |
+| 0.7429 | 10700 | 0.0002        |
+| 0.7498 | 10800 | 0.0002        |
+| 0.7568 | 10900 | 0.0002        |
+| 0.7637 | 11000 | 0.0002        |
+| 0.7707 | 11100 | 0.0002        |
+| 0.7776 | 11200 | 0.0002        |
+| 0.7846 | 11300 | 0.0002        |
+| 0.7915 | 11400 | 0.0002        |
+| 0.7984 | 11500 | 0.0002        |
+| 0.8054 | 11600 | 0.0002        |
+| 0.8123 | 11700 | 0.0002        |
+| 0.8193 | 11800 | 0.0002        |
+| 0.8262 | 11900 | 0.0002        |
+| 0.8332 | 12000 | 0.0002        |
+| 0.8401 | 12100 | 0.0002        |
+| 0.8470 | 12200 | 0.0002        |
+| 0.8540 | 12300 | 0.0002        |
+| 0.8609 | 12400 | 0.0002        |
+| 0.8679 | 12500 | 0.0002        |
+| 0.8748 | 12600 | 0.0002        |
+| 0.8818 | 12700 | 0.0001        |
+| 0.8887 | 12800 | 0.0002        |
+| 0.8956 | 12900 | 0.0002        |
+| 0.9026 | 13000 | 0.0002        |
+| 0.9095 | 13100 | 0.0001        |
+| 0.9165 | 13200 | 0.0002        |
+| 0.9234 | 13300 | 0.0002        |
+| 0.9304 | 13400 | 0.0002        |
+| 0.9373 | 13500 | 0.0001        |
+| 0.9442 | 13600 | 0.0002        |
+| 0.9512 | 13700 | 0.0002        |
+| 0.9581 | 13800 | 0.0001        |
+| 0.9651 | 13900 | 0.0001        |
+| 0.9720 | 14000 | 0.0002        |
+| 0.9790 | 14100 | 0.0002        |
+| 0.9859 | 14200 | 0.0001        |
+| 0.9928 | 14300 | 0.0001        |
+| 0.9998 | 14400 | 0.0001        |
+| 1.0067 | 14500 | 0.0001        |
+| 1.0137 | 14600 | 0.0001        |
+| 1.0206 | 14700 | 0.0001        |
+| 1.0276 | 14800 | 0.0002        |
+| 1.0345 | 14900 | 0.0001        |
+| 1.0414 | 15000 | 0.0002        |
+| 1.0484 | 15100 | 0.0002        |
+| 1.0553 | 15200 | 0.0001        |
+| 1.0623 | 15300 | 0.0002        |
+| 1.0692 | 15400 | 0.0001        |
+| 1.0762 | 15500 | 0.0001        |
+| 1.0831 | 15600 | 0.0001        |
+| 1.0901 | 15700 | 0.0001        |
+| 1.0970 | 15800 | 0.0001        |
+| 1.1039 | 15900 | 0.0001        |
+| 1.1109 | 16000 | 0.0001        |
+| 1.1178 | 16100 | 0.0002        |
+| 1.1248 | 16200 | 0.0001        |
+| 1.1317 | 16300 | 0.0002        |
+| 1.1387 | 16400 | 0.0001        |
+| 1.1456 | 16500 | 0.0001        |
+| 1.1525 | 16600 | 0.0001        |
+| 1.1595 | 16700 | 0.0001        |
+| 1.1664 | 16800 | 0.0001        |
+| 1.1734 | 16900 | 0.0001        |
+| 1.1803 | 17000 | 0.0002        |
+| 1.1873 | 17100 | 0.0001        |
+| 1.1942 | 17200 | 0.0001        |
+| 1.2011 | 17300 | 0.0001        |
+| 1.2081 | 17400 | 0.0001        |
+| 1.2150 | 17500 | 0.0001        |
+| 1.2220 | 17600 | 0.0001        |
+| 1.2289 | 17700 | 0.0001        |
+| 1.2359 | 17800 | 0.0001        |
+| 1.2428 | 17900 | 0.0001        |
+| 1.2497 | 18000 | 0.0001        |
+| 1.2567 | 18100 | 0.0001        |
+| 1.2636 | 18200 | 0.0001        |
+| 1.2706 | 18300 | 0.0001        |
+| 1.2775 | 18400 | 0.0001        |
+| 1.2845 | 18500 | 0.0001        |
+| 1.2914 | 18600 | 0.0001        |
+| 1.2983 | 18700 | 0.0001        |
+| 1.3053 | 18800 | 0.0001        |
+| 1.3122 | 18900 | 0.0001        |
+| 1.3192 | 19000 | 0.0001        |
+| 1.3261 | 19100 | 0.0001        |
+| 1.3331 | 19200 | 0.0001        |
+| 1.3400 | 19300 | 0.0001        |
+| 1.3469 | 19400 | 0.0001        |
+| 1.3539 | 19500 | 0.0001        |
+| 1.3608 | 19600 | 0.0001        |
+| 1.3678 | 19700 | 0.0001        |
+| 1.3747 | 19800 | 0.0001        |
+| 1.3817 | 19900 | 0.0001        |
+| 1.3886 | 20000 | 0.0001        |
+| 1.3955 | 20100 | 0.0001        |
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+| 4.9018 | 70600 | 0.0001        |
+| 4.9087 | 70700 | 0.0001        |
+| 4.9156 | 70800 | 0.0001        |
+| 4.9226 | 70900 | 0.0           |
+| 4.9295 | 71000 | 0.0001        |
+| 4.9365 | 71100 | 0.0001        |
+| 4.9434 | 71200 | 0.0           |
+| 4.9504 | 71300 | 0.0001        |
+| 4.9573 | 71400 | 0.0           |
+| 4.9642 | 71500 | 0.0           |
+| 4.9712 | 71600 | 0.0           |
+| 4.9781 | 71700 | 0.0001        |
+| 4.9851 | 71800 | 0.0           |
+| 4.9920 | 71900 | 0.0001        |
+| 4.9990 | 72000 | 0.0           |
+
+</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",
+}
+```
+
+<!--
+## 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"
+}

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
+}