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neuropark/sahajBERT-NCC

sourceHugging Faceapache-2.0updated 5y agoView on Hugging Face
2likes27downloads
Model Card

language: bn tags:

  • —collaborative
  • —bengali
  • —SequenceClassification license: apache-2.0 datasets: IndicGlue metrics:
  • —Loss
  • —Accuracy
  • —Precision
  • —Recall widget:
  • —text: "এশিয়ায় প্রথম দৃষ্টিহীন ব্যক্তির মাউন্ট এভারেস্ট জয়|"

sahajBERT News Article Classification

Model description

sahajBERT fine-tuned for news article classification using the sna.bn split of IndicGlue.

The model is trained for classifying articles into 5 different classes:

Label idLabel
0kolkata
1state
2national
3sports
4entertainment
5international

Intended uses & limitations

How to use

You can use this model directly with a pipeline for Sequence Classification:

python
from transformers import AlbertForSequenceClassification, TextClassificationPipeline, PreTrainedTokenizerFast

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

# Initialize model
model = AlbertForSequenceClassification.from_pretrained("neuropark/sahajBERT-NCC")

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

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

<!-- Provide examples of latent issues and potential remediations. --> WIP

Training data

The model was initialized with pre-trained weights of sahajBERT at step 19519 and trained on the sna.bn split of IndicGlue.

Training procedure

Coming soon! <!-- ```bibtex @inproceedings{..., year={2020} }

-->

## Eval results


Loss: 0.2477145493030548

Accuracy: 0.926293408929837

Macro F1: 0.9079785326650756

Recall: 0.926293408929837

Weighted F1: 0.9266428029354202

Macro Precision: 0.9109938492260489

Micro Precision: 0.926293408929837

Weighted Precision: 0.9288535478995414

Macro Recall: 0.9069095007692186

Micro Recall: 0.926293408929837

Weighted Recall: 0.926293408929837


### BibTeX entry and citation info

Coming soon! 
<!-- ```bibtex
@inproceedings{...,
  year={2020}
}