Add new SentenceTransformer model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

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 +1,775 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:131157
+- loss:MultipleNegativesRankingLoss
+base_model: intfloat/multilingual-e5-small
+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 intfloat/multilingual-e5-small
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small). 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:** [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) <!-- at revision c007d7ef6fd86656326059b28395a7a03a7c5846 -->
+- **Maximum Sequence Length:** 512 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': 512, '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/eFuck")
+# 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 آمار بهتر استفاده کنم؟',
+    'من به دنبال خر��د 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: 6 tokens</li><li>mean: 16.81 tokens</li><li>max: 97 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.59 tokens</li><li>max: 59 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
+
+- `per_device_train_batch_size`: 32
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.005
+- `num_train_epochs`: 10
+- `warmup_ratio`: 0.1
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `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`: 0.005
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 10
+- `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`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `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 |
+|:------:|:-----:|:-------------:|
+| 0.0244 | 100   | 1.3984        |
+| 0.0488 | 200   | 0.8762        |
+| 0.0732 | 300   | 0.2492        |
+| 0.0976 | 400   | 0.0754        |
+| 0.1220 | 500   | 0.0809        |
+| 0.1464 | 600   | 0.0789        |
+| 0.1708 | 700   | 0.076         |
+| 0.1952 | 800   | 0.0642        |
+| 0.2196 | 900   | 0.0743        |
+| 0.2440 | 1000  | 0.0605        |
+| 0.2684 | 1100  | 0.0705        |
+| 0.2928 | 1200  | 0.0594        |
+| 0.3172 | 1300  | 0.0565        |
+| 0.3415 | 1400  | 0.071         |
+| 0.3659 | 1500  | 0.0476        |
+| 0.3903 | 1600  | 0.0514        |
+| 0.4147 | 1700  | 0.0584        |
+| 0.4391 | 1800  | 0.0649        |
+| 0.4635 | 1900  | 0.0485        |
+| 0.4879 | 2000  | 0.0556        |
+| 0.5123 | 2100  | 0.0594        |
+| 0.5367 | 2200  | 0.0556        |
+| 0.5611 | 2300  | 0.0439        |
+| 0.5855 | 2400  | 0.0619        |
+| 0.6099 | 2500  | 0.0553        |
+| 0.6343 | 2600  | 0.0393        |
+| 0.6587 | 2700  | 0.0458        |
+| 0.6831 | 2800  | 0.0476        |
+| 0.7075 | 2900  | 0.0535        |
+| 0.7319 | 3000  | 0.0439        |
+| 0.7563 | 3100  | 0.0438        |
+| 0.7807 | 3200  | 0.052         |
+| 0.8051 | 3300  | 0.0514        |
+| 0.8295 | 3400  | 0.0549        |
+| 0.8539 | 3500  | 0.0439        |
+| 0.8783 | 3600  | 0.0429        |
+| 0.9027 | 3700  | 0.0442        |
+| 0.9271 | 3800  | 0.0643        |
+| 0.9515 | 3900  | 0.0408        |
+| 0.9758 | 4000  | 0.0403        |
+| 1.0002 | 4100  | 0.0446        |
+| 1.0246 | 4200  | 0.0527        |
+| 1.0490 | 4300  | 0.0545        |
+| 1.0734 | 4400  | 0.0517        |
+| 1.0978 | 4500  | 0.0299        |
+| 1.1222 | 4600  | 0.0444        |
+| 1.1466 | 4700  | 0.0475        |
+| 1.1710 | 4800  | 0.0414        |
+| 1.1954 | 4900  | 0.0386        |
+| 1.2198 | 5000  | 0.0508        |
+| 1.2442 | 5100  | 0.0384        |
+| 1.2686 | 5200  | 0.0453        |
+| 1.2930 | 5300  | 0.0401        |
+| 1.3174 | 5400  | 0.0328        |
+| 1.3418 | 5500  | 0.0456        |
+| 1.3662 | 5600  | 0.0295        |
+| 1.3906 | 5700  | 0.0366        |
+| 1.4150 | 5800  | 0.0431        |
+| 1.4394 | 5900  | 0.0442        |
+| 1.4638 | 6000  | 0.0343        |
+| 1.4882 | 6100  | 0.0405        |
+| 1.5126 | 6200  | 0.0357        |
+| 1.5370 | 6300  | 0.0423        |
+| 1.5614 | 6400  | 0.0288        |
+| 1.5858 | 6500  | 0.0393        |
+| 1.6101 | 6600  | 0.0369        |
+| 1.6345 | 6700  | 0.0245        |
+| 1.6589 | 6800  | 0.0286        |
+| 1.6833 | 6900  | 0.0325        |
+| 1.7077 | 7000  | 0.0311        |
+| 1.7321 | 7100  | 0.0272        |
+| 1.7565 | 7200  | 0.0261        |
+| 1.7809 | 7300  | 0.0296        |
+| 1.8053 | 7400  | 0.0343        |
+| 1.8297 | 7500  | 0.036         |
+| 1.8541 | 7600  | 0.0225        |
+| 1.8785 | 7700  | 0.0232        |
+| 1.9029 | 7800  | 0.0275        |
+| 1.9273 | 7900  | 0.0394        |
+| 1.9517 | 8000  | 0.0297        |
+| 1.9761 | 8100  | 0.0249        |
+| 2.0005 | 8200  | 0.0268        |
+| 2.0249 | 8300  | 0.0269        |
+| 2.0493 | 8400  | 0.0296        |
+| 2.0737 | 8500  | 0.0326        |
+| 2.0981 | 8600  | 0.0183        |
+| 2.1225 | 8700  | 0.024         |
+| 2.1469 | 8800  | 0.0298        |
+| 2.1713 | 8900  | 0.0273        |
+| 2.1957 | 9000  | 0.0244        |
+| 2.2201 | 9100  | 0.0308        |
+| 2.2444 | 9200  | 0.0247        |
+| 2.2688 | 9300  | 0.0299        |
+| 2.2932 | 9400  | 0.0222        |
+| 2.3176 | 9500  | 0.0213        |
+| 2.3420 | 9600  | 0.0316        |
+| 2.3664 | 9700  | 0.0157        |
+| 2.3908 | 9800  | 0.0248        |
+| 2.4152 | 9900  | 0.028         |
+| 2.4396 | 10000 | 0.0269        |
+| 2.4640 | 10100 | 0.0214        |
+| 2.4884 | 10200 | 0.0242        |
+| 2.5128 | 10300 | 0.0222        |
+| 2.5372 | 10400 | 0.0253        |
+| 2.5616 | 10500 | 0.0175        |
+| 2.5860 | 10600 | 0.0269        |
+| 2.6104 | 10700 | 0.0281        |
+| 2.6348 | 10800 | 0.014         |
+| 2.6592 | 10900 | 0.0187        |
+| 2.6836 | 11000 | 0.0204        |
+| 2.7080 | 11100 | 0.0228        |
+| 2.7324 | 11200 | 0.0193        |
+| 2.7568 | 11300 | 0.014         |
+| 2.7812 | 11400 | 0.0171        |
+| 2.8056 | 11500 | 0.0213        |
+| 2.8300 | 11600 | 0.025         |
+| 2.8544 | 11700 | 0.0138        |
+| 2.8788 | 11800 | 0.0133        |
+| 2.9031 | 11900 | 0.021         |
+| 2.9275 | 12000 | 0.0256        |
+| 2.9519 | 12100 | 0.019         |
+| 2.9763 | 12200 | 0.0149        |
+| 3.0007 | 12300 | 0.0192        |
+| 3.0251 | 12400 | 0.0194        |
+| 3.0495 | 12500 | 0.0179        |
+| 3.0739 | 12600 | 0.0218        |
+| 3.0983 | 12700 | 0.0126        |
+| 3.1227 | 12800 | 0.018         |
+| 3.1471 | 12900 | 0.0188        |
+| 3.1715 | 13000 | 0.0181        |
+| 3.1959 | 13100 | 0.0186        |
+| 3.2203 | 13200 | 0.0235        |
+| 3.2447 | 13300 | 0.0172        |
+| 3.2691 | 13400 | 0.0183        |
+| 3.2935 | 13500 | 0.0155        |
+| 3.3179 | 13600 | 0.0135        |
+| 3.3423 | 13700 | 0.0236        |
+| 3.3667 | 13800 | 0.0115        |
+| 3.3911 | 13900 | 0.0162        |
+| 3.4155 | 14000 | 0.0207        |
+| 3.4399 | 14100 | 0.0174        |
+| 3.4643 | 14200 | 0.0128        |
+| 3.4887 | 14300 | 0.0202        |
+| 3.5131 | 14400 | 0.0165        |
+| 3.5374 | 14500 | 0.0162        |
+| 3.5618 | 14600 | 0.015         |
+| 3.5862 | 14700 | 0.0203        |
+| 3.6106 | 14800 | 0.0222        |
+| 3.6350 | 14900 | 0.0105        |
+| 3.6594 | 15000 | 0.014         |
+| 3.6838 | 15100 | 0.0146        |
+| 3.7082 | 15200 | 0.015         |
+| 3.7326 | 15300 | 0.0153        |
+| 3.7570 | 15400 | 0.0099        |
+| 3.7814 | 15500 | 0.0105        |
+| 3.8058 | 15600 | 0.0168        |
+| 3.8302 | 15700 | 0.0185        |
+| 3.8546 | 15800 | 0.0104        |
+| 3.8790 | 15900 | 0.01          |
+| 3.9034 | 16000 | 0.0142        |
+| 3.9278 | 16100 | 0.0197        |
+| 3.9522 | 16200 | 0.013         |
+| 3.9766 | 16300 | 0.0137        |
+| 4.0010 | 16400 | 0.0133        |
+| 4.0254 | 16500 | 0.0132        |
+| 4.0498 | 16600 | 0.0124        |
+| 4.0742 | 16700 | 0.0141        |
+| 4.0986 | 16800 | 0.0099        |
+| 4.1230 | 16900 | 0.0113        |
+| 4.1474 | 17000 | 0.0149        |
+| 4.1717 | 17100 | 0.0145        |
+| 4.1961 | 17200 | 0.0129        |
+| 4.2205 | 17300 | 0.0185        |
+| 4.2449 | 17400 | 0.0138        |
+| 4.2693 | 17500 | 0.0133        |
+| 4.2937 | 17600 | 0.0107        |
+| 4.3181 | 17700 | 0.0092        |
+| 4.3425 | 17800 | 0.0175        |
+| 4.3669 | 17900 | 0.0097        |
+| 4.3913 | 18000 | 0.0111        |
+| 4.4157 | 18100 | 0.0136        |
+| 4.4401 | 18200 | 0.0122        |
+| 4.4645 | 18300 | 0.0095        |
+| 4.4889 | 18400 | 0.0141        |
+| 4.5133 | 18500 | 0.0094        |
+| 4.5377 | 18600 | 0.0123        |
+| 4.5621 | 18700 | 0.0108        |
+| 4.5865 | 18800 | 0.0145        |
+| 4.6109 | 18900 | 0.0195        |
+| 4.6353 | 19000 | 0.0099        |
+| 4.6597 | 19100 | 0.0107        |
+| 4.6841 | 19200 | 0.0105        |
+| 4.7085 | 19300 | 0.0124        |
+| 4.7329 | 19400 | 0.012         |
+| 4.7573 | 19500 | 0.0081        |
+| 4.7817 | 19600 | 0.0081        |
+| 4.8061 | 19700 | 0.0111        |
+| 4.8304 | 19800 | 0.0141        |
+| 4.8548 | 19900 | 0.0073        |
+| 4.8792 | 20000 | 0.0094        |
+| 4.9036 | 20100 | 0.011         |
+| 4.9280 | 20200 | 0.0157        |
+| 4.9524 | 20300 | 0.0086        |
+| 4.9768 | 20400 | 0.0093        |
+| 5.0012 | 20500 | 0.011         |
+| 5.0256 | 20600 | 0.0107        |
+| 5.0500 | 20700 | 0.0094        |
+| 5.0744 | 20800 | 0.008         |
+| 5.0988 | 20900 | 0.0076        |
+| 5.1232 | 21000 | 0.0088        |
+| 5.1476 | 21100 | 0.0119        |
+| 5.1720 | 21200 | 0.0118        |
+| 5.1964 | 21300 | 0.0105        |
+| 5.2208 | 21400 | 0.0138        |
+| 5.2452 | 21500 | 0.0109        |
+| 5.2696 | 21600 | 0.0101        |
+| 5.2940 | 21700 | 0.008         |
+| 5.3184 | 21800 | 0.0068        |
+| 5.3428 | 21900 | 0.0123        |
+| 5.3672 | 22000 | 0.0086        |
+| 5.3916 | 22100 | 0.0084        |
+| 5.4160 | 22200 | 0.0113        |
+| 5.4404 | 22300 | 0.0086        |
+| 5.4647 | 22400 | 0.0076        |
+| 5.4891 | 22500 | 0.0101        |
+| 5.5135 | 22600 | 0.0083        |
+| 5.5379 | 22700 | 0.0116        |
+| 5.5623 | 22800 | 0.0083        |
+| 5.5867 | 22900 | 0.0137        |
+| 5.6111 | 23000 | 0.0144        |
+| 5.6355 | 23100 | 0.0081        |
+| 5.6599 | 23200 | 0.006         |
+| 5.6843 | 23300 | 0.0096        |
+| 5.7087 | 23400 | 0.0098        |
+| 5.7331 | 23500 | 0.0096        |
+| 5.7575 | 23600 | 0.0063        |
+| 5.7819 | 23700 | 0.0052        |
+| 5.8063 | 23800 | 0.008         |
+| 5.8307 | 23900 | 0.0117        |
+| 5.8551 | 24000 | 0.0053        |
+| 5.8795 | 24100 | 0.0077        |
+| 5.9039 | 24200 | 0.0086        |
+| 5.9283 | 24300 | 0.0129        |
+| 5.9527 | 24400 | 0.0085        |
+| 5.9771 | 24500 | 0.0064        |
+| 6.0015 | 24600 | 0.0092        |
+| 6.0259 | 24700 | 0.0076        |
+| 6.0503 | 24800 | 0.0078        |
+| 6.0747 | 24900 | 0.0074        |
+| 6.0990 | 25000 | 0.0064        |
+| 6.1234 | 25100 | 0.0067        |
+| 6.1478 | 25200 | 0.0091        |
+| 6.1722 | 25300 | 0.0087        |
+| 6.1966 | 25400 | 0.0076        |
+| 6.2210 | 25500 | 0.0104        |
+| 6.2454 | 25600 | 0.0077        |
+| 6.2698 | 25700 | 0.0074        |
+| 6.2942 | 25800 | 0.0055        |
+| 6.3186 | 25900 | 0.0059        |
+| 6.3430 | 26000 | 0.0092        |
+| 6.3674 | 26100 | 0.0051        |
+| 6.3918 | 26200 | 0.0075        |
+| 6.4162 | 26300 | 0.0093        |
+| 6.4406 | 26400 | 0.0073        |
+| 6.4650 | 26500 | 0.0051        |
+| 6.4894 | 26600 | 0.0093        |
+| 6.5138 | 26700 | 0.0065        |
+| 6.5382 | 26800 | 0.0072        |
+| 6.5626 | 26900 | 0.0075        |
+| 6.5870 | 27000 | 0.0111        |
+| 6.6114 | 27100 | 0.0139        |
+| 6.6358 | 27200 | 0.0066        |
+| 6.6602 | 27300 | 0.0062        |
+| 6.6846 | 27400 | 0.0078        |
+| 6.7090 | 27500 | 0.0084        |
+| 6.7333 | 27600 | 0.0077        |
+| 6.7577 | 27700 | 0.0055        |
+| 6.7821 | 27800 | 0.0039        |
+| 6.8065 | 27900 | 0.0082        |
+| 6.8309 | 28000 | 0.0101        |
+| 6.8553 | 28100 | 0.0041        |
+| 6.8797 | 28200 | 0.0058        |
+| 6.9041 | 28300 | 0.0058        |
+| 6.9285 | 28400 | 0.0109        |
+| 6.9529 | 28500 | 0.0054        |
+| 6.9773 | 28600 | 0.0061        |
+| 7.0017 | 28700 | 0.0078        |
+| 7.0261 | 28800 | 0.0065        |
+| 7.0505 | 28900 | 0.0061        |
+| 7.0749 | 29000 | 0.0049        |
+| 7.0993 | 29100 | 0.0062        |
+| 7.1237 | 29200 | 0.0052        |
+| 7.1481 | 29300 | 0.0073        |
+| 7.1725 | 29400 | 0.0072        |
+| 7.1969 | 29500 | 0.0067        |
+| 7.2213 | 29600 | 0.0093        |
+| 7.2457 | 29700 | 0.008         |
+| 7.2701 | 29800 | 0.0057        |
+| 7.2945 | 29900 | 0.0051        |
+| 7.3189 | 30000 | 0.0046        |
+| 7.3433 | 30100 | 0.0078        |
+| 7.3677 | 30200 | 0.0041        |
+| 7.3920 | 30300 | 0.0054        |
+| 7.4164 | 30400 | 0.008         |
+| 7.4408 | 30500 | 0.0056        |
+| 7.4652 | 30600 | 0.0037        |
+| 7.4896 | 30700 | 0.0071        |
+| 7.5140 | 30800 | 0.0058        |
+| 7.5384 | 30900 | 0.0074        |
+| 7.5628 | 31000 | 0.0059        |
+| 7.5872 | 31100 | 0.0088        |
+| 7.6116 | 31200 | 0.0102        |
+| 7.6360 | 31300 | 0.0058        |
+| 7.6604 | 31400 | 0.0044        |
+| 7.6848 | 31500 | 0.0065        |
+| 7.7092 | 31600 | 0.007         |
+| 7.7336 | 31700 | 0.0078        |
+| 7.7580 | 31800 | 0.0048        |
+| 7.7824 | 31900 | 0.0033        |
+| 7.8068 | 32000 | 0.0063        |
+| 7.8312 | 32100 | 0.008         |
+| 7.8556 | 32200 | 0.004         |
+| 7.8800 | 32300 | 0.0057        |
+| 7.9044 | 32400 | 0.005         |
+| 7.9288 | 32500 | 0.0095        |
+| 7.9532 | 32600 | 0.0042        |
+| 7.9776 | 32700 | 0.0058        |
+| 8.0020 | 32800 | 0.006         |
+| 8.0263 | 32900 | 0.006         |
+| 8.0507 | 33000 | 0.0054        |
+| 8.0751 | 33100 | 0.0041        |
+| 8.0995 | 33200 | 0.0045        |
+| 8.1239 | 33300 | 0.0052        |
+| 8.1483 | 33400 | 0.0067        |
+| 8.1727 | 33500 | 0.008         |
+| 8.1971 | 33600 | 0.0047        |
+| 8.2215 | 33700 | 0.0079        |
+| 8.2459 | 33800 | 0.0071        |
+| 8.2703 | 33900 | 0.0043        |
+| 8.2947 | 34000 | 0.0041        |
+| 8.3191 | 34100 | 0.0035        |
+| 8.3435 | 34200 | 0.0059        |
+| 8.3679 | 34300 | 0.004         |
+| 8.3923 | 34400 | 0.005         |
+| 8.4167 | 34500 | 0.0067        |
+| 8.4411 | 34600 | 0.0049        |
+| 8.4655 | 34700 | 0.0034        |
+| 8.4899 | 34800 | 0.0057        |
+| 8.5143 | 34900 | 0.0052        |
+| 8.5387 | 35000 | 0.005         |
+| 8.5631 | 35100 | 0.0047        |
+| 8.5875 | 35200 | 0.0089        |
+| 8.6119 | 35300 | 0.0066        |
+| 8.6363 | 35400 | 0.0044        |
+| 8.6606 | 35500 | 0.0037        |
+| 8.6850 | 35600 | 0.0059        |
+| 8.7094 | 35700 | 0.0069        |
+| 8.7338 | 35800 | 0.0069        |
+| 8.7582 | 35900 | 0.0038        |
+| 8.7826 | 36000 | 0.0028        |
+| 8.8070 | 36100 | 0.0047        |
+| 8.8314 | 36200 | 0.007         |
+| 8.8558 | 36300 | 0.0036        |
+| 8.8802 | 36400 | 0.0049        |
+| 8.9046 | 36500 | 0.0041        |
+| 8.9290 | 36600 | 0.0085        |
+| 8.9534 | 36700 | 0.004         |
+| 8.9778 | 36800 | 0.0044        |
+| 9.0022 | 36900 | 0.0053        |
+| 9.0266 | 37000 | 0.006         |
+| 9.0510 | 37100 | 0.0051        |
+| 9.0754 | 37200 | 0.0029        |
+| 9.0998 | 37300 | 0.0041        |
+| 9.1242 | 37400 | 0.0046        |
+| 9.1486 | 37500 | 0.0057        |
+| 9.1730 | 37600 | 0.0063        |
+| 9.1974 | 37700 | 0.0048        |
+| 9.2218 | 37800 | 0.0077        |
+| 9.2462 | 37900 | 0.0056        |
+| 9.2706 | 38000 | 0.0039        |
+| 9.2949 | 38100 | 0.0036        |
+| 9.3193 | 38200 | 0.0032        |
+| 9.3437 | 38300 | 0.0055        |
+| 9.3681 | 38400 | 0.0037        |
+| 9.3925 | 38500 | 0.0045        |
+| 9.4169 | 38600 | 0.0065        |
+| 9.4413 | 38700 | 0.0047        |
+| 9.4657 | 38800 | 0.0033        |
+| 9.4901 | 38900 | 0.0052        |
+| 9.5145 | 39000 | 0.0043        |
+| 9.5389 | 39100 | 0.0043        |
+| 9.5633 | 39200 | 0.0049        |
+| 9.5877 | 39300 | 0.0074        |
+| 9.6121 | 39400 | 0.0054        |
+| 9.6365 | 39500 | 0.004         |
+| 9.6609 | 39600 | 0.0031        |
+| 9.6853 | 39700 | 0.0054        |
+| 9.7097 | 39800 | 0.0061        |
+| 9.7341 | 39900 | 0.0055        |
+| 9.7585 | 40000 | 0.0033        |
+| 9.7829 | 40100 | 0.0028        |
+| 9.8073 | 40200 | 0.0046        |
+| 9.8317 | 40300 | 0.0062        |
+| 9.8561 | 40400 | 0.0033        |
+| 9.8805 | 40500 | 0.0047        |
+| 9.9049 | 40600 | 0.0045        |
+| 9.9293 | 40700 | 0.0075        |
+| 9.9536 | 40800 | 0.0035        |
+| 9.9780 | 40900 | 0.0038        |
+
+</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: 4.0.0
+- 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.json

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "intfloat/multilingual-e5-small",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250037
+}

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

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c21a1606b9d53642bcb9f06f7cd6ab913e4d8bd59cf578c715cce7d13b851c7c
+size 470637416

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": 512,
+  "do_lower_case": false
+}

sentencepiece.bpe.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
+size 5069051

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ef04f2b385d1514f500e779207ace0f53e30895ce37563179e29f4022d28ca38
+size 17083053

tokenizer_config.json

@@ -0,0 +1,56 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "250001": {
+      "content": "<mask>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}