farhana1996/unsupervised-simcse-bangla-bert-base

SentenceTransformer based on sagorsarker/bangla-bert-base

This is a sentence-transformers model finetuned from sagorsarker/bangla-bert-base. 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: sagorsarker/bangla-bert-base
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("farhana1996/unsupervised-simcse-bangla-bert-base")
# Run inference
sentences = [
    'শুক্রবার বিকালে সিলেটের ফেঞ্চুগঞ্জের উত্তর ইসলামপুরে  বন্যার্ত পরিবারের মধ্যে ত্রাণ বিতরণ কার্যক্রমের উদ্বোধন শেষে সাংবাদিকদের তিনি একথা জানান',
    'থিওরি ও সূত্রগুলো ভালোভাবে বুঝতে  হবে',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [2, 768]

Training Details

Training Dataset

• Size: 500,000 training samples

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• num_train_epochs: 1
• multi_dataset_batch_sampler: round_robin

Environment

• 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