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
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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

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:cdb5e457c257c79d26e3e2155b70a62d35f57d3039f04d7a896ecf482fa27602
+size 2362528

README.md

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+---
+base_model: sentence-transformers/LaBSE
+datasets: []
+language: []
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:22151
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: 3 . Estimated cost of the project is Rs . 11 ,076 .48 Cr . and
+    project will be completed in 5 years .
+  sentences:
+  - প্রোজেক্ত অসিদা চংগনি হায়না পানরিবা শেনফম্না লুপা ক্রোর ১১ ,০৭৬.৪৮নি অমসুং মসি
+    চহি ৫দা মপুং ফানা লোইশিনগনি ।
+  - বেসিক ত্রেনিং প্রোভাইদরশীংগী ইলিজিবিলিতি
+  - সর্ভিস ভোটরশীং অসি মখোয়গী য়ুমগী এদ্রেস অদুগী রেসিদেন্টনি হায়না লৌগনি ।
+- source_sentence: The Prime Minister , Shri Narendra Modi has congratulated Aanchal
+    Thakur on winning India’s first international medal in skiing at FIS International
+    Skiing Competition in Turkey .
+  sentences:
+  - করিগুম্বা মথক্তা পনখ্রিবা কম্পোষ্টিংগী ফিভমশীং অসি ঙাক্লবদি ,  কম্পোষ্ট অদুদা
+    ফিজিকেল পেরামিটর খরা উবা ফংবদা নুমিৎ হুম্ফুনিগী  (  নুমিৎ ৬০  )   মতম চংগনি ।
+  - নহাক্না TV মুত্থৎপা মতমদা HD সেট তোপ বোক্স অদু প্লগ পোইন্টতা স্বিটচ ওফ তৌ ।
+  - তর্কীদা পাংথোকপা এফআইএস ইন্তরনেস্নেল স্কাইং কম্পিতিসন্দা স্কাইংদা ভারতকী অহানবা
+    অন্তরজাতিগী তকমান লৌরকপদা প্রধানমন্ত্রী শ্রী নরেন্দ্র মোদীনা আঞ্চল ঠাকুরবু থাগৎপা
+    ফোংদোকখ্রে ।
+- source_sentence: motorized traditional ratt
+  sentences:
+  - মোটোরাইজ ত্রেদিস্নেল রাট
+  - ভারতনা এপ্রোচ তৌরিবা অদুদি য়ু.এন.এফ.সি.সি.সি.গী প্রিন্সিপলশিং অমসুং প্রোভিজনশিং
+    অমসুং ইক্ব্যুইতী অমসুং কমন বত দিফরেনসিয়েতেদ রেস্পোন্সিবিলিতীজ এন্দ রেস্পেক্তিব
+    কেপাবিলিতী ( সি.বি.পি.আর-আর.সি. ) না গাইদ তৌবনি ।
+  - প্রধান মন্ত্রী শ্রী নরেন্দ্র মোদীনা অহল ওইরবা পাউমী অমসুং হান্নগী রাজ্য সভাগী
+    মীহুৎ ওইবীরম্বা কুলদীপ নায়রনা লৈখিদবদা অৱাবা ফোংদোকখ্রে ।
+- source_sentence: His decision making ability infused in him the strength to overcome
+    all obstacles .
+  sentences:
+  - প্রধান মন্ত্রীনা হান্নগী রাস্ত্রপতি মোহমদ নশীদকসু ৱারী শান্নখি অমদি মহাক্কী মায়
+    পাক্লকপদসু নুংঙাইবা ফোংদোকখি ।
+  - রিলিফ এমপ্লোয়মেন্ট
+  - অমসুং মরম অসিনা মহাক্কী মপোক নুমিৎ অসি ‘রাষ্ট্রীয় এক্তা দিবস’ হায়না পাংথোক্লিবনি
+    ।
+- source_sentence: additional channel for banking and key catalyst for financial inclusion
+  sentences:
+  - বেঙ্কিংগী অহেনবা চেনেল অমসুং ফাইনান্সিএল ইনক্লুজনগীদমক্তা মরুওইবা কেটালিষ্ট  অমা
+    ওই ।
+  - মসিগা মান্ননা ,  কম্প্যুটর সিষ্টেমশীংদা পাক-চাউনা অমাং-অতা থোকহনগদবা মাং-তাক্নিংঙাই
+    ওইবা কম্প্যুটর প্রোগ্রাম শেম্বা অমসুং শন্দোকপা হায়বসিসু সাইবরক্রাইমগী অতোপ্পা
+    মখল অমনি ।
+  - 7. মহাক্কী অখন্নবা অতিথি অমা ওইনা রাস্ত্রপতি সোলি ৱাশক লৌবগী থৌরম শরুক য়ানবা মহাক্না
+    হন্দক মালদিব্সতা চৎলুবা খোঙচৎ অদু প্রধান মন্ত্রী মোদীনা নিংশিংখি ।
+---
+
+# SentenceTransformer based on sentence-transformers/LaBSE
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) <!-- at revision e34fab64a3011d2176c99545a93d5cbddc9a91b7 -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 768 tokens
+- **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("ABHIiiii1/LaBSE-Fine-Tuned-EN-MN")
+# Run inference
+sentences = [
+    'additional channel for banking and key catalyst for financial inclusion',
+    'বেঙ্কিংগী অহেনবা চেনেল অমসুং ফাইনান্সিএল ইনক্লুজনগীদমক্তা মরুওইবা কেটালিষ্ট  অমা ওই ।',
+    '7. মহাক্কী অখন্নবা অতিথি অমা ওইনা রাস্ত্রপতি সোলি ৱাশক লৌবগী থৌরম শরুক য়ানবা মহাক্না হন্দক মালদিব্সতা চৎলুবা খোঙচৎ অদু প্রধান মন্ত্রী মোদীনা নিংশিংখি ।',
+]
+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: 22,151 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: 21.12 tokens</li><li>max: 73 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 49.95 tokens</li><li>max: 196 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                                       | sentence_1                                                                                                                                           |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>The Prime Minister , Shri Narendra Modi , today launched the health assurance scheme : Ayushman Bharat – Pradhan Mantri Jan Arogya Yojana – at Ranchi , Jharkhand .</code> | <code>ঙসি প্রধান মন্ত্রী নরেন্দ্র মোদীনা ঝারখান্দগী রাঞ্চীদা হেল্থ ইন্সুরেন্স স্কিম : আয়ুশ্মান ভারত-প্রধান মন্ত্রী জন অরোগ্য য়োজনা হৌদোক্লে ।</code> |
+  | <code>the portal provides information about all these topics</code>                                                                                                              | <code>পোর্টেল অসিদা হিরম পুম্নমক অসিগী মতাংদা ঈ-পাউ পীরি ।</code>                                                                                    |
+  | <code>The Prime Minister said that during the implementation of GST , there was active follow up on complaints and suggestions .</code>                                          | <code>জি এস তি ইমপ্লিমেন্ত তৌবা মতম অদুদা ৱাকৎশিং অমসুং পাউতাকশিংদা এক্তিব ওইনা ফোল্লো অপ তৌখি হায়না প্রধান মন্ত্রীনা হায়খি ।</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`: 16
+- `per_device_eval_batch_size`: 16
+- `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`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_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`: 3
+- `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`: 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`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `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
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.3610 | 500  | 0.2968        |
+| 0.7220 | 1000 | 0.1414        |
+| 1.0830 | 1500 | 0.1005        |
+| 1.4440 | 2000 | 0.0483        |
+| 1.8051 | 2500 | 0.0346        |
+| 2.1661 | 3000 | 0.0229        |
+| 2.5271 | 3500 | 0.0121        |
+| 2.8881 | 4000 | 0.0085        |
+
+
+### Framework Versions
+- Python: 3.10.13
+- Sentence Transformers: 3.0.1
+- Transformers: 4.42.3
+- PyTorch: 2.1.2
+- Accelerate: 0.32.1
+- Datasets: 2.20.0
+- Tokenizers: 0.19.1
+
+## 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 +1,32 @@
+{
+  "_name_or_path": "sentence-transformers/LaBSE",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
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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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