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:

Intended uses & limitations

How to use

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

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}
}
``` -->