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 sha256:821108d08fcc8640cca5249a8e39f843fe39a4a231fe600d1598f544d528bbd2
+size 2362528

README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:1021596
+- loss:MultipleNegativesRankingLoss
+base_model: codersan/FaLabse
+widget:
+- source_sentence: Most women can't understand why this happens.
+  sentences:
+  - 'بیشتر زنان دلیل این کار را درک نمی‌کنند '
+  - ' سخت از خود در غضب بود که آن چه را به آسانی و صراحت می‌توانست نزد خود تصمیم بگیرد،
+    قادر به بیان آن در حضور شاهزاده خانم تورسکی نیست. زیرا این زن در نظر او تجسم همان
+    نیروی بیدادگری بود که بر زندگی ظاهری او حکومت می‌کرد و مانع ابراز عشق و عفو و
+    نمایاندن احساساتش بود.'
+  - 'آقای تالبویز: چه روزهای خوشی، عجب روزهای ‌خوشی!'
+- source_sentence: to government offices, to the post office, and to the Governor's.
+  sentences:
+  - ناخوشی را تقویت می‌کند.
+  - به ادارات دولتی و اداره پست و سپس نزد استاندار رفت.
+  - اما به حال طبیعی نبود و در حالی که بازوی شوهرش را گرفته بود، گفتی که در عالم رؤیا
+    قدم بر میدارد.
+- source_sentence: Even as she did so a sound checked her for an instant ' the unmistakable
+    bang of a window shutting, somewhere in Mrs Semprill's house.
+  sentences:
+  - در همین آن صدائی به گوشش رسید که بدون شک صدای بسته شدن ‌پنجره خانه خانم سمپریل
+    بود!
+  - این کارم گذشتن از مرز بود.
+  - به همین دلیل هیچ کس بهتر از او برای تربیت مردی که حافظ تمامی خصوصیات نیک خانوادگی
+    باشد، وجود نداشت.
+- source_sentence: 'It signifies God: done this day by my hand.'
+  sentences:
+  - معنی آن مهر این است که 3 خدا، امروز به دست من انجام شد.
+  - همه یکدیگر را بوسیدند
+  - این نشو نه‌ی جادوگرهای تبه کاره
+- source_sentence: If this were continued, the barricade was no longer tenable.
+  sentences:
+  - اگر این کار مداومت می‌یافت، سنگر قادر به مقاومت نمی‌بود.
+  - هر دو با هم به زمین می‌غلتیدند.
+  - خوب، در این لحظه او یک محافظ داشت.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on codersan/FaLabse
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [codersan/FaLabse](https://huggingface.co/codersan/FaLabse). 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:** [codersan/FaLabse](https://huggingface.co/codersan/FaLabse) <!-- at revision 0fe1341c6962d7fe2ea375d90f9f55f34e395bcd -->
+- **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/FaLabse_Mizan4")
+# Run inference
+sentences = [
+    'If this were continued, the barricade was no longer tenable.',
+    'اگر این کار مداومت می\u200cیافت، سنگر قادر به مقاومت نمی\u200cبود.',
+    'خوب، در این لحظه او یک م��افظ داشت.',
+]
+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: 1,021,596 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: 3 tokens</li><li>mean: 16.37 tokens</li><li>max: 85 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 18.63 tokens</li><li>max: 81 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                 | positive                                                                                                                  |
+  |:-------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------|
+  | <code>They arose to obey.</code>                                                                       | <code>دختران برای اطاعت امر پدر از جا برخاستند.</code>                                                                    |
+  | <code>You'll know it all in time</code>                                                                | <code>همه چیز را بم وقع خواهی دانست.</code>                                                                               |
+  | <code>She is in hysterics up there, and moans and says that we have been 'shamed and disgraced.</code> | <code>او هر لحظه گرفتار یک‌ وضع است، زارزار گریه می‌کند. می‌گوید به ما توهین کرده‌اند، حیثیتمان را لکه‌دار نمودند.</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`: 32
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `load_best_model_at_end`: True
+- `push_to_hub`: True
+- `hub_model_id`: codersan/FaLabse_Mizan4
+- `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`: 32
+- `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
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `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`: 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`: True
+- `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/FaLabse_Mizan4
+- `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       | -             |
+| 0.0031     | 100     | 0.1023        |
+| 0.0063     | 200     | 0.1162        |
+| 0.0094     | 300     | 0.0976        |
+| **0.0125** | **400** | **0.088**     |
+| 0.0157     | 500     | 0.0691        |
+| 0.0188     | 600     | 0.0678        |
+| 0.0219     | 700     | 0.082         |
+| 0.0251     | 800     | 0.08          |
+| 0.0282     | 900     | 0.0758        |
+| 0.0313     | 1000    | 0.0763        |
+| 0.0345     | 1100    | 0.0786        |
+| 0.0376     | 1200    | 0.0666        |
+| 0.0407     | 1300    | 0.0722        |
+| 0.0439     | 1400    | 0.0638        |
+| 0.0470     | 1500    | 0.0615        |
+| 0.0501     | 1600    | 0.0623        |
+| 0.0532     | 1700    | 0.0639        |
+| 0.0564     | 1800    | 0.0692        |
+| 0.0595     | 1900    | 0.0625        |
+| 0.0626     | 2000    | 0.0774        |
+| 0.0658     | 2100    | 0.06          |
+| 0.0689     | 2200    | 0.0543        |
+| 0.0720     | 2300    | 0.0611        |
+| 0.0752     | 2400    | 0.0697        |
+| 0.0783     | 2500    | 0.0703        |
+| 0.0814     | 2600    | 0.058         |
+| 0.0846     | 2700    | 0.075         |
+| 0.0877     | 2800    | 0.062         |
+| 0.0908     | 2900    | 0.0756        |
+| 0.0940     | 3000    | 0.0668        |
+| 0.0971     | 3100    | 0.054         |
+| 0.1002     | 3200    | 0.0626        |
+| 0.1034     | 3300    | 0.0645        |
+| 0.1065     | 3400    | 0.0714        |
+| 0.1096     | 3500    | 0.0644        |
+| 0.1128     | 3600    | 0.0693        |
+| 0.1159     | 3700    | 0.0734        |
+| 0.1190     | 3800    | 0.0622        |
+| 0.1222     | 3900    | 0.0741        |
+| 0.1253     | 4000    | 0.0761        |
+| 0.1284     | 4100    | 0.0582        |
+| 0.1316     | 4200    | 0.0804        |
+| 0.1347     | 4300    | 0.0708        |
+| 0.1378     | 4400    | 0.0734        |
+| 0.1410     | 4500    | 0.0709        |
+| 0.1441     | 4600    | 0.0759        |
+| 0.1472     | 4700    | 0.085         |
+| 0.1504     | 4800    | 0.0573        |
+| 0.1535     | 4900    | 0.056         |
+| 0.1566     | 5000    | 0.0601        |
+| 0.1597     | 5100    | 0.0596        |
+| 0.1629     | 5200    | 0.079         |
+| 0.1660     | 5300    | 0.0679        |
+| 0.1691     | 5400    | 0.0553        |
+| 0.1723     | 5500    | 0.0677        |
+| 0.1754     | 5600    | 0.0795        |
+| 0.1785     | 5700    | 0.0779        |
+| 0.1817     | 5800    | 0.0599        |
+| 0.1848     | 5900    | 0.0667        |
+| 0.1879     | 6000    | 0.064         |
+| 0.1911     | 6100    | 0.0637        |
+| 0.1942     | 6200    | 0.0747        |
+| 0.1973     | 6300    | 0.0829        |
+| 0.2005     | 6400    | 0.0589        |
+| 0.2036     | 6500    | 0.0623        |
+| 0.2067     | 6600    | 0.0589        |
+| 0.2099     | 6700    | 0.0648        |
+| 0.2130     | 6800    | 0.0527        |
+| 0.2161     | 6900    | 0.0519        |
+| 0.2193     | 7000    | 0.0668        |
+| 0.2224     | 7100    | 0.0729        |
+| 0.2255     | 7200    | 0.0627        |
+| 0.2287     | 7300    | 0.0539        |
+| 0.2318     | 7400    | 0.055         |
+| 0.2349     | 7500    | 0.0663        |
+| 0.2381     | 7600    | 0.0589        |
+| 0.2412     | 7700    | 0.0555        |
+| 0.2443     | 7800    | 0.0875        |
+| 0.2475     | 7900    | 0.055         |
+| 0.2506     | 8000    | 0.0584        |
+| 0.2537     | 8100    | 0.0607        |
+| 0.2569     | 8200    | 0.0551        |
+| 0.2600     | 8300    | 0.0527        |
+| 0.2631     | 8400    | 0.0773        |
+| 0.2662     | 8500    | 0.0696        |
+| 0.2694     | 8600    | 0.062         |
+| 0.2725     | 8700    | 0.0716        |
+| 0.2756     | 8800    | 0.06          |
+| 0.2788     | 8900    | 0.0536        |
+| 0.2819     | 9000    | 0.0604        |
+| 0.2850     | 9100    | 0.0563        |
+| 0.2882     | 9200    | 0.0734        |
+| 0.2913     | 9300    | 0.0714        |
+| 0.2944     | 9400    | 0.0658        |
+| 0.2976     | 9500    | 0.0623        |
+| 0.3007     | 9600    | 0.0713        |
+| 0.3038     | 9700    | 0.0674        |
+| 0.3070     | 9800    | 0.0708        |
+| 0.3101     | 9900    | 0.0579        |
+| 0.3132     | 10000   | 0.0616        |
+| 0.3164     | 10100   | 0.0653        |
+| 0.3195     | 10200   | 0.0614        |
+| 0.3226     | 10300   | 0.0626        |
+| 0.3258     | 10400   | 0.0611        |
+| 0.3289     | 10500   | 0.0521        |
+| 0.3320     | 10600   | 0.056         |
+| 0.3352     | 10700   | 0.0761        |
+| 0.3383     | 10800   | 0.0629        |
+| 0.3414     | 10900   | 0.0658        |
+| 0.3446     | 11000   | 0.0576        |
+| 0.3477     | 11100   | 0.0483        |
+| 0.3508     | 11200   | 0.0654        |
+| 0.3540     | 11300   | 0.0602        |
+| 0.3571     | 11400   | 0.065         |
+| 0.3602     | 11500   | 0.0787        |
+| 0.3634     | 11600   | 0.0634        |
+| 0.3665     | 11700   | 0.0678        |
+| 0.3696     | 11800   | 0.0758        |
+| 0.3727     | 11900   | 0.0637        |
+| 0.3759     | 12000   | 0.0577        |
+| 0.3790     | 12100   | 0.0572        |
+| 0.3821     | 12200   | 0.0614        |
+| 0.3853     | 12300   | 0.0685        |
+| 0.3884     | 12400   | 0.0641        |
+| 0.3915     | 12500   | 0.0583        |
+| 0.3947     | 12600   | 0.0502        |
+| 0.3978     | 12700   | 0.0481        |
+| 0.4009     | 12800   | 0.0546        |
+| 0.4041     | 12900   | 0.0664        |
+| 0.4072     | 13000   | 0.0699        |
+| 0.4103     | 13100   | 0.0513        |
+| 0.4135     | 13200   | 0.0423        |
+| 0.4166     | 13300   | 0.0554        |
+| 0.4197     | 13400   | 0.0592        |
+| 0.4229     | 13500   | 0.0457        |
+| 0.4260     | 13600   | 0.0612        |
+| 0.4291     | 13700   | 0.0507        |
+| 0.4323     | 13800   | 0.0592        |
+| 0.4354     | 13900   | 0.0566        |
+| 0.4385     | 14000   | 0.0806        |
+| 0.4417     | 14100   | 0.0648        |
+| 0.4448     | 14200   | 0.0535        |
+| 0.4479     | 14300   | 0.0748        |
+| 0.4511     | 14400   | 0.0488        |
+| 0.4542     | 14500   | 0.0539        |
+| 0.4573     | 14600   | 0.0597        |
+| 0.4605     | 14700   | 0.065         |
+| 0.4636     | 14800   | 0.0594        |
+| 0.4667     | 14900   | 0.05          |
+| 0.4699     | 15000   | 0.0488        |
+| 0.4730     | 15100   | 0.0537        |
+| 0.4761     | 15200   | 0.0396        |
+| 0.4792     | 15300   | 0.0616        |
+| 0.4824     | 15400   | 0.0605        |
+| 0.4855     | 15500   | 0.0599        |
+| 0.4886     | 15600   | 0.0616        |
+| 0.4918     | 15700   | 0.0731        |
+| 0.4949     | 15800   | 0.0654        |
+| 0.4980     | 15900   | 0.0463        |
+| 0.5012     | 16000   | 0.0463        |
+| 0.5043     | 16100   | 0.0594        |
+| 0.5074     | 16200   | 0.0575        |
+| 0.5106     | 16300   | 0.056         |
+| 0.5137     | 16400   | 0.0542        |
+| 0.5168     | 16500   | 0.052         |
+| 0.5200     | 16600   | 0.0438        |
+| 0.5231     | 16700   | 0.0675        |
+| 0.5262     | 16800   | 0.0619        |
+| 0.5294     | 16900   | 0.0515        |
+| 0.5325     | 17000   | 0.0575        |
+| 0.5356     | 17100   | 0.0568        |
+| 0.5388     | 17200   | 0.0508        |
+| 0.5419     | 17300   | 0.059         |
+| 0.5450     | 17400   | 0.0505        |
+| 0.5482     | 17500   | 0.0582        |
+| 0.5513     | 17600   | 0.0574        |
+| 0.5544     | 17700   | 0.0613        |
+| 0.5576     | 17800   | 0.048         |
+| 0.5607     | 17900   | 0.0553        |
+| 0.5638     | 18000   | 0.0571        |
+| 0.5670     | 18100   | 0.0543        |
+| 0.5701     | 18200   | 0.0484        |
+| 0.5732     | 18300   | 0.0763        |
+| 0.5764     | 18400   | 0.056         |
+| 0.5795     | 18500   | 0.0533        |
+| 0.5826     | 18600   | 0.044         |
+| 0.5857     | 18700   | 0.0515        |
+| 0.5889     | 18800   | 0.0516        |
+| 0.5920     | 18900   | 0.0586        |
+| 0.5951     | 19000   | 0.0523        |
+| 0.5983     | 19100   | 0.0733        |
+| 0.6014     | 19200   | 0.0453        |
+| 0.6045     | 19300   | 0.0663        |
+| 0.6077     | 19400   | 0.0381        |
+| 0.6108     | 19500   | 0.0568        |
+| 0.6139     | 19600   | 0.0492        |
+| 0.6171     | 19700   | 0.0489        |
+| 0.6202     | 19800   | 0.0575        |
+| 0.6233     | 19900   | 0.0642        |
+| 0.6265     | 20000   | 0.0535        |
+| 0.6296     | 20100   | 0.0598        |
+| 0.6327     | 20200   | 0.0569        |
+| 0.6359     | 20300   | 0.0513        |
+| 0.6390     | 20400   | 0.0515        |
+| 0.6421     | 20500   | 0.053         |
+| 0.6453     | 20600   | 0.0569        |
+| 0.6484     | 20700   | 0.0372        |
+| 0.6515     | 20800   | 0.0464        |
+| 0.6547     | 20900   | 0.0522        |
+| 0.6578     | 21000   | 0.0427        |
+| 0.6609     | 21100   | 0.0584        |
+| 0.6641     | 21200   | 0.0616        |
+| 0.6672     | 21300   | 0.0552        |
+| 0.6703     | 21400   | 0.0509        |
+| 0.6735     | 21500   | 0.0439        |
+| 0.6766     | 21600   | 0.0762        |
+| 0.6797     | 21700   | 0.0539        |
+| 0.6829     | 21800   | 0.0475        |
+| 0.6860     | 21900   | 0.0557        |
+| 0.6891     | 22000   | 0.0421        |
+| 0.6922     | 22100   | 0.0471        |
+| 0.6954     | 22200   | 0.0398        |
+| 0.6985     | 22300   | 0.0521        |
+| 0.7016     | 22400   | 0.0472        |
+| 0.7048     | 22500   | 0.0579        |
+| 0.7079     | 22600   | 0.0539        |
+| 0.7110     | 22700   | 0.0527        |
+| 0.7142     | 22800   | 0.0677        |
+| 0.7173     | 22900   | 0.0509        |
+| 0.7204     | 23000   | 0.0478        |
+| 0.7236     | 23100   | 0.0593        |
+| 0.7267     | 23200   | 0.0419        |
+| 0.7298     | 23300   | 0.0576        |
+| 0.7330     | 23400   | 0.0485        |
+| 0.7361     | 23500   | 0.0544        |
+| 0.7392     | 23600   | 0.0537        |
+| 0.7424     | 23700   | 0.0481        |
+| 0.7455     | 23800   | 0.0597        |
+| 0.7486     | 23900   | 0.0464        |
+| 0.7518     | 24000   | 0.0537        |
+| 0.7549     | 24100   | 0.0508        |
+| 0.7580     | 24200   | 0.045         |
+| 0.7612     | 24300   | 0.0337        |
+| 0.7643     | 24400   | 0.0478        |
+| 0.7674     | 24500   | 0.0495        |
+| 0.7706     | 24600   | 0.0427        |
+| 0.7737     | 24700   | 0.0596        |
+| 0.7768     | 24800   | 0.0468        |
+| 0.7800     | 24900   | 0.0404        |
+| 0.7831     | 25000   | 0.0467        |
+| 0.7862     | 25100   | 0.0514        |
+| 0.7894     | 25200   | 0.0462        |
+| 0.7925     | 25300   | 0.0401        |
+| 0.7956     | 25400   | 0.0539        |
+| 0.7987     | 25500   | 0.0541        |
+| 0.8019     | 25600   | 0.0639        |
+| 0.8050     | 25700   | 0.0392        |
+| 0.8081     | 25800   | 0.0466        |
+| 0.8113     | 25900   | 0.0543        |
+| 0.8144     | 26000   | 0.0507        |
+| 0.8175     | 26100   | 0.0465        |
+| 0.8207     | 26200   | 0.0386        |
+| 0.8238     | 26300   | 0.0606        |
+| 0.8269     | 26400   | 0.0558        |
+| 0.8301     | 26500   | 0.0488        |
+| 0.8332     | 26600   | 0.0556        |
+| 0.8363     | 26700   | 0.047         |
+| 0.8395     | 26800   | 0.0548        |
+| 0.8426     | 26900   | 0.0423        |
+| 0.8457     | 27000   | 0.0529        |
+| 0.8489     | 27100   | 0.0513        |
+| 0.8520     | 27200   | 0.0432        |
+| 0.8551     | 27300   | 0.0605        |
+| 0.8583     | 27400   | 0.0448        |
+| 0.8614     | 27500   | 0.0508        |
+| 0.8645     | 27600   | 0.0578        |
+| 0.8677     | 27700   | 0.0409        |
+| 0.8708     | 27800   | 0.0487        |
+| 0.8739     | 27900   | 0.058         |
+| 0.8771     | 28000   | 0.0461        |
+| 0.8802     | 28100   | 0.0389        |
+| 0.8833     | 28200   | 0.0427        |
+| 0.8865     | 28300   | 0.0473        |
+| 0.8896     | 28400   | 0.061         |
+| 0.8927     | 28500   | 0.0423        |
+| 0.8958     | 28600   | 0.0435        |
+| 0.8990     | 28700   | 0.0389        |
+| 0.9021     | 28800   | 0.0466        |
+| 0.9052     | 28900   | 0.042         |
+| 0.9084     | 29000   | 0.0466        |
+| 0.9115     | 29100   | 0.0412        |
+| 0.9146     | 29200   | 0.0444        |
+| 0.9178     | 29300   | 0.059         |
+| 0.9209     | 29400   | 0.0466        |
+| 0.9240     | 29500   | 0.0381        |
+| 0.9272     | 29600   | 0.0408        |
+| 0.9303     | 29700   | 0.0557        |
+| 0.9334     | 29800   | 0.0567        |
+| 0.9366     | 29900   | 0.0537        |
+| 0.9397     | 30000   | 0.041         |
+| 0.9428     | 30100   | 0.0383        |
+| 0.9460     | 30200   | 0.0412        |
+| 0.9491     | 30300   | 0.0489        |
+| 0.9522     | 30400   | 0.046         |
+| 0.9554     | 30500   | 0.0525        |
+| 0.9585     | 30600   | 0.0493        |
+| 0.9616     | 30700   | 0.0485        |
+| 0.9648     | 30800   | 0.0532        |
+| 0.9679     | 30900   | 0.0446        |
+| 0.9710     | 31000   | 0.0372        |
+| 0.9742     | 31100   | 0.0472        |
+| 0.9773     | 31200   | 0.0399        |
+| 0.9804     | 31300   | 0.0402        |
+| 0.9836     | 31400   | 0.0372        |
+| 0.9867     | 31500   | 0.0497        |
+| 0.9898     | 31600   | 0.0432        |
+| 0.9930     | 31700   | 0.0382        |
+| 0.9961     | 31800   | 0.0475        |
+| 0.9992     | 31900   | 0.0367        |
+
+* The bold row denotes the saved checkpoint.
+</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"
+}

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
+}