Sahajbert NER

🚀 sahajBERT Named Entity Recognition

The sahajBERT model fine-tuned for Bengali Named Entity Recognition (NER), enabling collaborative NER tasks in the Bengali language.

🚀 Quick Start

You can use this model directly with a pipeline for token classification:

from transformers import AlbertForTokenClassification, TokenClassificationPipeline, PreTrainedTokenizerFast

# Initialize tokenizer
tokenizer = PreTrainedTokenizerFast.from_pretrained("neuropark/sahajBERT-NER")

# Initialize model
model = AlbertForTokenClassification.from_pretrained("neuropark/sahajBERT-NER")

# Initialize pipeline
pipeline = TokenClassificationPipeline(tokenizer=tokenizer, model=model)

raw_text = "এই ইউনিয়নে ৩ টি মৌজা ও ১০ টি গ্রাম আছে ।" # Change me
output = pipeline(raw_text)

✨ Features

• Collaborative: Suitable for collaborative NER tasks.
• Bengali Support: Specifically fine - tuned for Bengali language NER.
• Multiple Metrics: Evaluated using Loss, Accuracy, Precision, Recall, and F1 score.

📚 Documentation

Model description

sahajBERT fine - tuned for NER using the bengali split of WikiANN .

Named Entities predicted by the model:

Intended uses & limitations

How to use

As shown in the quick start section, you can use this model directly with a pipeline for token classification.

Limitations and bias

WIP

Training data

The model was initialized with pre - trained weights of sahajBERT at step 19519 and trained on the bengali split of WikiANN

Training procedure

Coming soon!

Eval results

BibTeX entry and citation info

Coming soon!

📄 License

This model is licensed under the Apache 2.0 license.