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": 768,
+  "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 +1,869 @@
+---
+base_model: shihab17/bangla-sentence-transformer
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:1000000
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: ফেসবুক পোস্টে জোসেফ রাধিক জানিয়েছেন, প্রথম থেকেই ফটোগ্রাফির নেশা
+    ছিল তার
+  sentences:
+  - ফেসবুক পোস্টে জোসেফ রাধিক জানিয়েছেন, প্রথম থেকেই ফটোগ্রাফির নেশা ছিল তার
+  - গত বছর সিউল অলিম্পিক স্টেডিয়ামে হাজির হয়েছিলেন  হাজার দর্শক
+  - বিশ্বের সবচেয়ে প্রবীণ পুরুষ জাপানের সাকারি মোমোই  বছর বয়সে মারা গেছেন
+- source_sentence: তিনি যুগান্তরকে বলেন,  মাগুরা জেলার রাজনৈতিক কর্মকাণ্ড আবর্তিত
+    হচ্ছে সাইফুজ্জামান শিখরকে ঘিরেই
+  sentences:
+  - যাবার আগে বলে গেলেন আসি
+  - তিনি যুগান্তরকে বলেন,  মাগুরা জেলার রাজনৈতিক কর্মকাণ্ড আবর্তিত হচ্ছে সাইফুজ্জামান
+    শিখরকে ঘিরেই
+  - ওই কর্মকর্তা বলেন, সাম্প্রতিক জঙ্গি হামলার পরিপ্রেক্ষিতে ওই সময়ে টেলিযোগাযোগকে
+    কীভাবে কাজে লাগানো যায় তার অংশ হিসেবে এ মহড়া হবে বলে তাদেরকে জানানো হয়েছে
+- source_sentence: এ স্বাধীনতা অর্জনের পথে সমগ্র জাতির সঙ্গে একই কাতারে পাবনার বেড়া
+    অঞ্চলের হাজারো মানুষ যুদ্ধে অবতীর্ণ হন এবং অনেকেই শাহাদাত বরণ করেন
+  sentences:
+  - এ স্বাধীনতা অর্জনের পথে সমগ্র জাতির সঙ্গে একই কাতারে পাবনার বেড়া অঞ্চলের হাজারো
+    মানুষ যুদ্ধে অবতীর্ণ হন এবং অনেকেই শাহাদাত বরণ করেন
+  - বোরো মৌসুমের ধান কাটাতে হয়
+  - এই আতঙ্কের অবসান কে ঘটাবে? এখন আর কোনো সময় নেই
+- source_sentence: ওয়েস্ট ইন্ডিজের বিপক্ষে চার ইনিংসে বাংলাদেশের রান ছিল যথাক্রমে
+    , ,  ও
+  sentences:
+  - বাংলাদেশের অন্যতম সফল ব্যাটার আশরাফুল
+  - ওয়েস্ট ইন্ডিজের বিপক্ষে চার ইনিংসে বাংলাদেশের রান ছিল যথাক্রমে , ,  ও
+  - রয়টার্স
+- source_sentence: রোহিঙ্গা অনুপ্রবেশসহ বিভিন্ন ইস্যুতে নানা টানা পোড়েনের মধ্যেই
+    স্বরাষ্ট্রমন্ত্রী আসাদুজ্জামান খান কামাল মিয়ানমার সফরে যাচ্ছেন
+  sentences:
+  - এ পর্বে অতিথি হিসেবে উপস্থিত হবেন বীর মুক্তিযোদ্ধা এবং বিশিষ্ট সাংস্কৃতিক ব্যক্তিত্ব
+    নাসির উদ্দীন ইউসুফ এবং তার মেয়ে প্রযোজক ও মঞ্চ অভিনেত্রী এশা ইউসুফ
+  - আগামী এক মাসের মধ্যে এটি জনপ্রশাসন মন্ত্রণালয়ে পাঠানো হবে বলে সংশ্লিষ্ট সূত্র
+    জানিয়েছে
+  - রোহিঙ্গা অনুপ্রবেশসহ বিভিন্ন ইস্যুতে নানা টানা পোড়েনের মধ্যেই স্বরাষ্ট্রমন্ত্রী
+    আসাদুজ্জামান খান কামাল মিয়ানমার সফরে যাচ্ছেন
+---
+
+# SentenceTransformer based on shihab17/bangla-sentence-transformer
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [shihab17/bangla-sentence-transformer](https://huggingface.co/shihab17/bangla-sentence-transformer). 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:** [shihab17/bangla-sentence-transformer](https://huggingface.co/shihab17/bangla-sentence-transformer) <!-- at revision ab250a2c767638562cd3caa8c0017b106a481755 -->
+- **Maximum Sequence Length:** 512 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': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
+  (1): Pooling({'word_embedding_dimension': 768, '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})
+)
+```
+
+## 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("farhana1996/unsupervised-simcse-bangla-sbert")
+# Run inference
+sentences = [
+    'রোহিঙ্গা অনুপ্রবেশসহ বিভিন্ন ইস্যুতে নানা টানা পোড়েনের মধ্যেই স্বরাষ্ট্রমন্ত্রী আসাদুজ্জামান খান কামাল মিয়ানমার সফরে যাচ্ছেন',
+    'রোহিঙ্গা অনুপ্রবেশসহ বিভিন্ন ইস্যুতে নানা টানা পোড়েনের মধ্যেই স্বরাষ্ট্রমন্ত্রী আসাদুজ্জামান খান কামাল মিয়ানমার সফরে যাচ্ছেন',
+    'আগামী এক মাসের মধ্যে এটি জনপ্রশাসন মন্ত্রণালয়ে পাঠানো হবে বলে সংশ্লিষ্ট সূত্র জানিয়েছে',
+]
+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: 1,000,000 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                         | sentence_1                                                                         |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 25.91 tokens</li><li>max: 148 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 25.91 tokens</li><li>max: 148 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                                                        | sentence_1                                                                                                                                                                                        |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>বিনোদন ডেস্ক   অভিনেতা নির্মাতা জাহিদ হাসান ঈদ উপলক্ষে অভিনয় ও পরিচালনা নিয়ে ব্যস্ত সময় কাটাচ্ছেন</code>                                                                                 | <code>বিনোদন ডেস্ক   অভিনেতা নির্মাতা জাহিদ হাসান ঈদ উপলক্ষে অভিনয় ও পরিচালনা নিয়ে ব্যস্ত সময় কাটাচ্ছেন</code>                                                                                 |
+  | <code>আগামী এক মাসের মধ্যে এটি জনপ্রশাসন মন্ত্রণালয়ে পাঠানো হবে বলে সংশ্লিষ্ট সূত্র জানিয়েছে</code>                                                                                             | <code>আগামী এক মাসের মধ্যে এটি জনপ্রশাসন মন্ত্রণালয়ে পাঠানো হবে বলে সংশ্লিষ্ট সূত্র জানিয়েছে</code>                                                                                             |
+  | <code>বিশ্ববিদ্যালয় ভারপ্রাপ্ত রেজিস্ট্রার প্রফেসর ড  কামরুল হুদা বলেন, পুলিশ বিশ্ববিদ্যালয় প্রশাসনের কাছে তালিকা চাইলে বিশ্ববিদ্যালয়ের বিভিন্ন বিভাগে খোঁজ নিয়ে  জনের নাম পাওয়া যায়</code> | <code>বিশ্ববিদ্যালয় ভারপ্রাপ্ত রেজিস্ট্রার প্রফেসর ড  কামরুল হুদা বলেন, পুলিশ বিশ্ববিদ্যালয় প্রশাসনের কাছে তালিকা চাইলে বিশ্ববিদ্যালয়ের বিভিন্ন বিভাগে খোঁজ নিয়ে  জনের নাম পাওয়া যায়</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`: 4
+- `per_device_eval_batch_size`: 4
+- `num_train_epochs`: 1
+- `fp16`: True
+- `multi_dataset_batch_sampler`: round_robin
+
+#### 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`: 4
+- `per_device_eval_batch_size`: 4
+- `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-05
+- `weight_decay`: 0.0
+- `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`: 0.0
+- `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`: True
+- `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`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch | Step   | Training Loss |
+|:-----:|:------:|:-------------:|
+| 0.002 | 500    | 0.0036        |
+| 0.004 | 1000   | 0.0001        |
+| 0.006 | 1500   | 0.0           |
+| 0.008 | 2000   | 0.0001        |
+| 0.01  | 2500   | 0.0           |
+| 0.012 | 3000   | 0.0           |
+| 0.014 | 3500   | 0.0           |
+| 0.016 | 4000   | 0.0           |
+| 0.018 | 4500   | 0.0           |
+| 0.02  | 5000   | 0.0001        |
+| 0.022 | 5500   | 0.0           |
+| 0.024 | 6000   | 0.0           |
+| 0.026 | 6500   | 0.0           |
+| 0.028 | 7000   | 0.0           |
+| 0.03  | 7500   | 0.0           |
+| 0.032 | 8000   | 0.0           |
+| 0.034 | 8500   | 0.0           |
+| 0.036 | 9000   | 0.0           |
+| 0.038 | 9500   | 0.0           |
+| 0.04  | 10000  | 0.0001        |
+| 0.042 | 10500  | 0.0           |
+| 0.044 | 11000  | 0.0002        |
+| 0.046 | 11500  | 0.0           |
+| 0.048 | 12000  | 0.0           |
+| 0.05  | 12500  | 0.0           |
+| 0.052 | 13000  | 0.0           |
+| 0.054 | 13500  | 0.0           |
+| 0.056 | 14000  | 0.0           |
+| 0.058 | 14500  | 0.0006        |
+| 0.06  | 15000  | 0.0           |
+| 0.062 | 15500  | 0.0           |
+| 0.064 | 16000  | 0.0           |
+| 0.066 | 16500  | 0.0001        |
+| 0.068 | 17000  | 0.0           |
+| 0.07  | 17500  | 0.0           |
+| 0.072 | 18000  | 0.0           |
+| 0.074 | 18500  | 0.0           |
+| 0.076 | 19000  | 0.0           |
+| 0.078 | 19500  | 0.0           |
+| 0.08  | 20000  | 0.0           |
+| 0.082 | 20500  | 0.0           |
+| 0.084 | 21000  | 0.0004        |
+| 0.086 | 21500  | 0.0           |
+| 0.088 | 22000  | 0.0           |
+| 0.09  | 22500  | 0.0           |
+| 0.092 | 23000  | 0.0           |
+| 0.094 | 23500  | 0.0           |
+| 0.096 | 24000  | 0.0001        |
+| 0.098 | 24500  | 0.0           |
+| 0.1   | 25000  | 0.0           |
+| 0.102 | 25500  | 0.0001        |
+| 0.104 | 26000  | 0.0           |
+| 0.106 | 26500  | 0.0001        |
+| 0.108 | 27000  | 0.0           |
+| 0.11  | 27500  | 0.0           |
+| 0.112 | 28000  | 0.0           |
+| 0.114 | 28500  | 0.0           |
+| 0.116 | 29000  | 0.0           |
+| 0.118 | 29500  | 0.0007        |
+| 0.12  | 30000  | 0.0           |
+| 0.122 | 30500  | 0.0           |
+| 0.124 | 31000  | 0.0           |
+| 0.126 | 31500  | 0.0           |
+| 0.128 | 32000  | 0.0           |
+| 0.13  | 32500  | 0.0           |
+| 0.132 | 33000  | 0.0           |
+| 0.134 | 33500  | 0.0003        |
+| 0.136 | 34000  | 0.0           |
+| 0.138 | 34500  | 0.0001        |
+| 0.14  | 35000  | 0.0           |
+| 0.142 | 35500  | 0.0007        |
+| 0.144 | 36000  | 0.0001        |
+| 0.146 | 36500  | 0.0           |
+| 0.148 | 37000  | 0.0           |
+| 0.15  | 37500  | 0.0           |
+| 0.152 | 38000  | 0.0           |
+| 0.154 | 38500  | 0.0           |
+| 0.156 | 39000  | 0.0           |
+| 0.158 | 39500  | 0.0           |
+| 0.16  | 40000  | 0.0           |
+| 0.162 | 40500  | 0.0           |
+| 0.164 | 41000  | 0.0           |
+| 0.166 | 41500  | 0.0           |
+| 0.168 | 42000  | 0.0005        |
+| 0.17  | 42500  | 0.0           |
+| 0.172 | 43000  | 0.0           |
+| 0.174 | 43500  | 0.0           |
+| 0.176 | 44000  | 0.0           |
+| 0.178 | 44500  | 0.0           |
+| 0.18  | 45000  | 0.0           |
+| 0.182 | 45500  | 0.0           |
+| 0.184 | 46000  | 0.0           |
+| 0.186 | 46500  | 0.0           |
+| 0.188 | 47000  | 0.0           |
+| 0.19  | 47500  | 0.0           |
+| 0.192 | 48000  | 0.0           |
+| 0.194 | 48500  | 0.0           |
+| 0.196 | 49000  | 0.0002        |
+| 0.198 | 49500  | 0.0           |
+| 0.2   | 50000  | 0.0           |
+| 0.202 | 50500  | 0.0008        |
+| 0.204 | 51000  | 0.0           |
+| 0.206 | 51500  | 0.0           |
+| 0.208 | 52000  | 0.0           |
+| 0.21  | 52500  | 0.0           |
+| 0.212 | 53000  | 0.0           |
+| 0.214 | 53500  | 0.0           |
+| 0.216 | 54000  | 0.0           |
+| 0.218 | 54500  | 0.0           |
+| 0.22  | 55000  | 0.0           |
+| 0.222 | 55500  | 0.0           |
+| 0.224 | 56000  | 0.0           |
+| 0.226 | 56500  | 0.0           |
+| 0.228 | 57000  | 0.0           |
+| 0.23  | 57500  | 0.0           |
+| 0.232 | 58000  | 0.0001        |
+| 0.234 | 58500  | 0.0005        |
+| 0.236 | 59000  | 0.0           |
+| 0.238 | 59500  | 0.0           |
+| 0.24  | 60000  | 0.0           |
+| 0.242 | 60500  | 0.0           |
+| 0.244 | 61000  | 0.0           |
+| 0.246 | 61500  | 0.0           |
+| 0.248 | 62000  | 0.0           |
+| 0.25  | 62500  | 0.0           |
+| 0.252 | 63000  | 0.0           |
+| 0.254 | 63500  | 0.0           |
+| 0.256 | 64000  | 0.0001        |
+| 0.258 | 64500  | 0.0007        |
+| 0.26  | 65000  | 0.0           |
+| 0.262 | 65500  | 0.0           |
+| 0.264 | 66000  | 0.0           |
+| 0.266 | 66500  | 0.0           |
+| 0.268 | 67000  | 0.0003        |
+| 0.27  | 67500  | 0.0           |
+| 0.272 | 68000  | 0.0           |
+| 0.274 | 68500  | 0.0           |
+| 0.276 | 69000  | 0.0           |
+| 0.278 | 69500  | 0.0           |
+| 0.28  | 70000  | 0.0           |
+| 0.282 | 70500  | 0.0           |
+| 0.284 | 71000  | 0.0           |
+| 0.286 | 71500  | 0.0           |
+| 0.288 | 72000  | 0.0           |
+| 0.29  | 72500  | 0.0           |
+| 0.292 | 73000  | 0.0           |
+| 0.294 | 73500  | 0.0           |
+| 0.296 | 74000  | 0.0004        |
+| 0.298 | 74500  | 0.0           |
+| 0.3   | 75000  | 0.0           |
+| 0.302 | 75500  | 0.0           |
+| 0.304 | 76000  | 0.0           |
+| 0.306 | 76500  | 0.0           |
+| 0.308 | 77000  | 0.0           |
+| 0.31  | 77500  | 0.0           |
+| 0.312 | 78000  | 0.0           |
+| 0.314 | 78500  | 0.0           |
+| 0.316 | 79000  | 0.0           |
+| 0.318 | 79500  | 0.0           |
+| 0.32  | 80000  | 0.0           |
+| 0.322 | 80500  | 0.0           |
+| 0.324 | 81000  | 0.0           |
+| 0.326 | 81500  | 0.0           |
+| 0.328 | 82000  | 0.0           |
+| 0.33  | 82500  | 0.0           |
+| 0.332 | 83000  | 0.0           |
+| 0.334 | 83500  | 0.0           |
+| 0.336 | 84000  | 0.0           |
+| 0.338 | 84500  | 0.0           |
+| 0.34  | 85000  | 0.0           |
+| 0.342 | 85500  | 0.0           |
+| 0.344 | 86000  | 0.0           |
+| 0.346 | 86500  | 0.0           |
+| 0.348 | 87000  | 0.0           |
+| 0.35  | 87500  | 0.0           |
+| 0.352 | 88000  | 0.0002        |
+| 0.354 | 88500  | 0.0           |
+| 0.356 | 89000  | 0.0           |
+| 0.358 | 89500  | 0.0           |
+| 0.36  | 90000  | 0.0           |
+| 0.362 | 90500  | 0.0           |
+| 0.364 | 91000  | 0.0           |
+| 0.366 | 91500  | 0.0           |
+| 0.368 | 92000  | 0.0           |
+| 0.37  | 92500  | 0.0           |
+| 0.372 | 93000  | 0.0           |
+| 0.374 | 93500  | 0.0           |
+| 0.376 | 94000  | 0.0002        |
+| 0.378 | 94500  | 0.0           |
+| 0.38  | 95000  | 0.0           |
+| 0.382 | 95500  | 0.0           |
+| 0.384 | 96000  | 0.0001        |
+| 0.386 | 96500  | 0.0           |
+| 0.388 | 97000  | 0.0           |
+| 0.39  | 97500  | 0.0           |
+| 0.392 | 98000  | 0.0           |
+| 0.394 | 98500  | 0.0           |
+| 0.396 | 99000  | 0.0           |
+| 0.398 | 99500  | 0.0           |
+| 0.4   | 100000 | 0.0006        |
+| 0.402 | 100500 | 0.0           |
+| 0.404 | 101000 | 0.0           |
+| 0.406 | 101500 | 0.0           |
+| 0.408 | 102000 | 0.0           |
+| 0.41  | 102500 | 0.0           |
+| 0.412 | 103000 | 0.0           |
+| 0.414 | 103500 | 0.0           |
+| 0.416 | 104000 | 0.0           |
+| 0.418 | 104500 | 0.0           |
+| 0.42  | 105000 | 0.0           |
+| 0.422 | 105500 | 0.0           |
+| 0.424 | 106000 | 0.0           |
+| 0.426 | 106500 | 0.0           |
+| 0.428 | 107000 | 0.0           |
+| 0.43  | 107500 | 0.0           |
+| 0.432 | 108000 | 0.0           |
+| 0.434 | 108500 | 0.0           |
+| 0.436 | 109000 | 0.0           |
+| 0.438 | 109500 | 0.0           |
+| 0.44  | 110000 | 0.0           |
+| 0.442 | 110500 | 0.0           |
+| 0.444 | 111000 | 0.0           |
+| 0.446 | 111500 | 0.0           |
+| 0.448 | 112000 | 0.0           |
+| 0.45  | 112500 | 0.0           |
+| 0.452 | 113000 | 0.0           |
+| 0.454 | 113500 | 0.0           |
+| 0.456 | 114000 | 0.0           |
+| 0.458 | 114500 | 0.0           |
+| 0.46  | 115000 | 0.0           |
+| 0.462 | 115500 | 0.0001        |
+| 0.464 | 116000 | 0.0           |
+| 0.466 | 116500 | 0.0           |
+| 0.468 | 117000 | 0.0           |
+| 0.47  | 117500 | 0.0           |
+| 0.472 | 118000 | 0.0           |
+| 0.474 | 118500 | 0.0           |
+| 0.476 | 119000 | 0.0           |
+| 0.478 | 119500 | 0.0           |
+| 0.48  | 120000 | 0.0           |
+| 0.482 | 120500 | 0.0           |
+| 0.484 | 121000 | 0.0           |
+| 0.486 | 121500 | 0.0           |
+| 0.488 | 122000 | 0.0           |
+| 0.49  | 122500 | 0.0           |
+| 0.492 | 123000 | 0.0           |
+| 0.494 | 123500 | 0.0           |
+| 0.496 | 124000 | 0.001         |
+| 0.498 | 124500 | 0.0           |
+| 0.5   | 125000 | 0.0           |
+| 0.502 | 125500 | 0.0           |
+| 0.504 | 126000 | 0.0           |
+| 0.506 | 126500 | 0.0           |
+| 0.508 | 127000 | 0.0           |
+| 0.51  | 127500 | 0.0           |
+| 0.512 | 128000 | 0.0           |
+| 0.514 | 128500 | 0.0           |
+| 0.516 | 129000 | 0.0           |
+| 0.518 | 129500 | 0.0           |
+| 0.52  | 130000 | 0.0           |
+| 0.522 | 130500 | 0.0           |
+| 0.524 | 131000 | 0.0           |
+| 0.526 | 131500 | 0.0           |
+| 0.528 | 132000 | 0.0           |
+| 0.53  | 132500 | 0.0           |
+| 0.532 | 133000 | 0.0           |
+| 0.534 | 133500 | 0.0           |
+| 0.536 | 134000 | 0.0           |
+| 0.538 | 134500 | 0.0           |
+| 0.54  | 135000 | 0.0           |
+| 0.542 | 135500 | 0.0           |
+| 0.544 | 136000 | 0.0           |
+| 0.546 | 136500 | 0.0           |
+| 0.548 | 137000 | 0.0           |
+| 0.55  | 137500 | 0.0           |
+| 0.552 | 138000 | 0.0           |
+| 0.554 | 138500 | 0.0           |
+| 0.556 | 139000 | 0.0           |
+| 0.558 | 139500 | 0.0           |
+| 0.56  | 140000 | 0.0           |
+| 0.562 | 140500 | 0.0           |
+| 0.564 | 141000 | 0.0           |
+| 0.566 | 141500 | 0.0           |
+| 0.568 | 142000 | 0.0           |
+| 0.57  | 142500 | 0.0           |
+| 0.572 | 143000 | 0.0           |
+| 0.574 | 143500 | 0.0           |
+| 0.576 | 144000 | 0.0           |
+| 0.578 | 144500 | 0.0           |
+| 0.58  | 145000 | 0.0           |
+| 0.582 | 145500 | 0.0           |
+| 0.584 | 146000 | 0.0           |
+| 0.586 | 146500 | 0.0           |
+| 0.588 | 147000 | 0.0           |
+| 0.59  | 147500 | 0.0           |
+| 0.592 | 148000 | 0.0           |
+| 0.594 | 148500 | 0.0           |
+| 0.596 | 149000 | 0.0           |
+| 0.598 | 149500 | 0.0           |
+| 0.6   | 150000 | 0.0           |
+| 0.602 | 150500 | 0.0           |
+| 0.604 | 151000 | 0.0           |
+| 0.606 | 151500 | 0.0           |
+| 0.608 | 152000 | 0.0           |
+| 0.61  | 152500 | 0.0           |
+| 0.612 | 153000 | 0.0           |
+| 0.614 | 153500 | 0.0           |
+| 0.616 | 154000 | 0.0           |
+| 0.618 | 154500 | 0.0           |
+| 0.62  | 155000 | 0.0           |
+| 0.622 | 155500 | 0.0           |
+| 0.624 | 156000 | 0.0           |
+| 0.626 | 156500 | 0.0           |
+| 0.628 | 157000 | 0.0           |
+| 0.63  | 157500 | 0.0           |
+| 0.632 | 158000 | 0.0           |
+| 0.634 | 158500 | 0.0           |
+| 0.636 | 159000 | 0.0           |
+| 0.638 | 159500 | 0.0           |
+| 0.64  | 160000 | 0.0           |
+| 0.642 | 160500 | 0.0           |
+| 0.644 | 161000 | 0.0           |
+| 0.646 | 161500 | 0.0           |
+| 0.648 | 162000 | 0.0           |
+| 0.65  | 162500 | 0.0           |
+| 0.652 | 163000 | 0.0           |
+| 0.654 | 163500 | 0.0           |
+| 0.656 | 164000 | 0.0001        |
+| 0.658 | 164500 | 0.0           |
+| 0.66  | 165000 | 0.0           |
+| 0.662 | 165500 | 0.0           |
+| 0.664 | 166000 | 0.0           |
+| 0.666 | 166500 | 0.0           |
+| 0.668 | 167000 | 0.0           |
+| 0.67  | 167500 | 0.0           |
+| 0.672 | 168000 | 0.0           |
+| 0.674 | 168500 | 0.0           |
+| 0.676 | 169000 | 0.0           |
+| 0.678 | 169500 | 0.0           |
+| 0.68  | 170000 | 0.0           |
+| 0.682 | 170500 | 0.0           |
+| 0.684 | 171000 | 0.0           |
+| 0.686 | 171500 | 0.0           |
+| 0.688 | 172000 | 0.0           |
+| 0.69  | 172500 | 0.0           |
+| 0.692 | 173000 | 0.0           |
+| 0.694 | 173500 | 0.0           |
+| 0.696 | 174000 | 0.0           |
+| 0.698 | 174500 | 0.0           |
+| 0.7   | 175000 | 0.0           |
+| 0.702 | 175500 | 0.0           |
+| 0.704 | 176000 | 0.0           |
+| 0.706 | 176500 | 0.0           |
+| 0.708 | 177000 | 0.0           |
+| 0.71  | 177500 | 0.0           |
+| 0.712 | 178000 | 0.0           |
+| 0.714 | 178500 | 0.0           |
+| 0.716 | 179000 | 0.0           |
+| 0.718 | 179500 | 0.0           |
+| 0.72  | 180000 | 0.0           |
+| 0.722 | 180500 | 0.0           |
+| 0.724 | 181000 | 0.0           |
+| 0.726 | 181500 | 0.0           |
+| 0.728 | 182000 | 0.0007        |
+| 0.73  | 182500 | 0.0           |
+| 0.732 | 183000 | 0.0           |
+| 0.734 | 183500 | 0.0           |
+| 0.736 | 184000 | 0.0           |
+| 0.738 | 184500 | 0.0           |
+| 0.74  | 185000 | 0.0           |
+| 0.742 | 185500 | 0.0           |
+| 0.744 | 186000 | 0.0           |
+| 0.746 | 186500 | 0.0           |
+| 0.748 | 187000 | 0.0           |
+| 0.75  | 187500 | 0.0           |
+| 0.752 | 188000 | 0.0           |
+| 0.754 | 188500 | 0.0           |
+| 0.756 | 189000 | 0.0           |
+| 0.758 | 189500 | 0.0           |
+| 0.76  | 190000 | 0.0           |
+| 0.762 | 190500 | 0.0           |
+| 0.764 | 191000 | 0.0           |
+| 0.766 | 191500 | 0.0           |
+| 0.768 | 192000 | 0.0           |
+| 0.77  | 192500 | 0.0           |
+| 0.772 | 193000 | 0.0           |
+| 0.774 | 193500 | 0.0           |
+| 0.776 | 194000 | 0.0           |
+| 0.778 | 194500 | 0.0           |
+| 0.78  | 195000 | 0.0           |
+| 0.782 | 195500 | 0.0           |
+| 0.784 | 196000 | 0.0007        |
+| 0.786 | 196500 | 0.0           |
+| 0.788 | 197000 | 0.0           |
+| 0.79  | 197500 | 0.0           |
+| 0.792 | 198000 | 0.0           |
+| 0.794 | 198500 | 0.0           |
+| 0.796 | 199000 | 0.0           |
+| 0.798 | 199500 | 0.0           |
+| 0.8   | 200000 | 0.0           |
+| 0.802 | 200500 | 0.0           |
+| 0.804 | 201000 | 0.0           |
+| 0.806 | 201500 | 0.0           |
+| 0.808 | 202000 | 0.0           |
+| 0.81  | 202500 | 0.0           |
+| 0.812 | 203000 | 0.0           |
+| 0.814 | 203500 | 0.0           |
+| 0.816 | 204000 | 0.0           |
+| 0.818 | 204500 | 0.0           |
+| 0.82  | 205000 | 0.0           |
+| 0.822 | 205500 | 0.0           |
+| 0.824 | 206000 | 0.0           |
+| 0.826 | 206500 | 0.0           |
+| 0.828 | 207000 | 0.0           |
+| 0.83  | 207500 | 0.0           |
+| 0.832 | 208000 | 0.0           |
+| 0.834 | 208500 | 0.0           |
+| 0.836 | 209000 | 0.0           |
+| 0.838 | 209500 | 0.0           |
+| 0.84  | 210000 | 0.0           |
+| 0.842 | 210500 | 0.0           |
+| 0.844 | 211000 | 0.0           |
+| 0.846 | 211500 | 0.0           |
+| 0.848 | 212000 | 0.0           |
+| 0.85  | 212500 | 0.0           |
+| 0.852 | 213000 | 0.0           |
+| 0.854 | 213500 | 0.0           |
+| 0.856 | 214000 | 0.0           |
+| 0.858 | 214500 | 0.0           |
+| 0.86  | 215000 | 0.0           |
+| 0.862 | 215500 | 0.0           |
+| 0.864 | 216000 | 0.0           |
+| 0.866 | 216500 | 0.0           |
+| 0.868 | 217000 | 0.0           |
+| 0.87  | 217500 | 0.0           |
+| 0.872 | 218000 | 0.0           |
+| 0.874 | 218500 | 0.0           |
+| 0.876 | 219000 | 0.0           |
+| 0.878 | 219500 | 0.0           |
+| 0.88  | 220000 | 0.0001        |
+| 0.882 | 220500 | 0.0006        |
+| 0.884 | 221000 | 0.0           |
+| 0.886 | 221500 | 0.0           |
+| 0.888 | 222000 | 0.0           |
+| 0.89  | 222500 | 0.0           |
+| 0.892 | 223000 | 0.0           |
+| 0.894 | 223500 | 0.0           |
+| 0.896 | 224000 | 0.0           |
+| 0.898 | 224500 | 0.0           |
+| 0.9   | 225000 | 0.0           |
+| 0.902 | 225500 | 0.0           |
+| 0.904 | 226000 | 0.0           |
+| 0.906 | 226500 | 0.0           |
+| 0.908 | 227000 | 0.0           |
+| 0.91  | 227500 | 0.0           |
+| 0.912 | 228000 | 0.0           |
+| 0.914 | 228500 | 0.0           |
+| 0.916 | 229000 | 0.0           |
+| 0.918 | 229500 | 0.0           |
+| 0.92  | 230000 | 0.0           |
+| 0.922 | 230500 | 0.0           |
+| 0.924 | 231000 | 0.0           |
+| 0.926 | 231500 | 0.0           |
+| 0.928 | 232000 | 0.0           |
+| 0.93  | 232500 | 0.0           |
+| 0.932 | 233000 | 0.0           |
+| 0.934 | 233500 | 0.0           |
+| 0.936 | 234000 | 0.0           |
+| 0.938 | 234500 | 0.0           |
+| 0.94  | 235000 | 0.0           |
+| 0.942 | 235500 | 0.0           |
+| 0.944 | 236000 | 0.0           |
+| 0.946 | 236500 | 0.0           |
+| 0.948 | 237000 | 0.0           |
+| 0.95  | 237500 | 0.0           |
+| 0.952 | 238000 | 0.0           |
+| 0.954 | 238500 | 0.0           |
+| 0.956 | 239000 | 0.0           |
+| 0.958 | 239500 | 0.0           |
+| 0.96  | 240000 | 0.0           |
+| 0.962 | 240500 | 0.0           |
+| 0.964 | 241000 | 0.0           |
+| 0.966 | 241500 | 0.0           |
+| 0.968 | 242000 | 0.0           |
+| 0.97  | 242500 | 0.0           |
+| 0.972 | 243000 | 0.0           |
+| 0.974 | 243500 | 0.0           |
+| 0.976 | 244000 | 0.0           |
+| 0.978 | 244500 | 0.0           |
+| 0.98  | 245000 | 0.0           |
+| 0.982 | 245500 | 0.0           |
+| 0.984 | 246000 | 0.0           |
+| 0.986 | 246500 | 0.0           |
+| 0.988 | 247000 | 0.0           |
+| 0.99  | 247500 | 0.0           |
+| 0.992 | 248000 | 0.0           |
+| 0.994 | 248500 | 0.0           |
+| 0.996 | 249000 | 0.0           |
+| 0.998 | 249500 | 0.0           |
+| 1.0   | 250000 | 0.0           |
+
+</details>
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 3.4.1
+- Transformers: 4.48.2
+- PyTorch: 2.4.1+cu121
+- Accelerate: 0.34.2
+- Datasets: 3.0.1
+- 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 +1,28 @@
+{
+  "_name_or_path": "unsupervised-simcse-bangla-sbert",
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.5,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
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config_sentence_transformers.json

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modules.json

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sentence_bert_config.json

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special_tokens_map.json

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tokenizer_config.json

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