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--- |
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language: bn |
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tags: |
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- collaborative |
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- bengali |
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- SequenceClassification |
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license: apache-2.0 |
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datasets: IndicGlue |
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metrics: |
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- Loss |
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- Accuracy |
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- Precision |
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- Recall |
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widget: |
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- text: "এশিয়ায় প্রথম দৃষ্টিহীন ব্যক্তির মাউন্ট এভারেস্ট জয়|" |
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--- |
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# sahajBERT News Article Classification |
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## Model description |
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[sahajBERT](https://huggingface.co/neuropark/sahajBERT) fine-tuned for news article classification using the `sna.bn` split of [IndicGlue](https://huggingface.co/datasets/indic_glue). |
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The model is trained for classifying articles into 5 different classes: |
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| Label id | Label | |
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|:--------:|:----:| |
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|0 | kolkata| |
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|1 | state| |
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|2 | national| |
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|3 | sports| |
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|4 | entertainment| |
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|5 | international| |
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## Intended uses & limitations |
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#### How to use |
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You can use this model directly with a pipeline for Sequence Classification: |
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```python |
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from transformers import AlbertForSequenceClassification, TextClassificationPipeline, PreTrainedTokenizerFast |
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# Initialize tokenizer |
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tokenizer = PreTrainedTokenizerFast.from_pretrained("neuropark/sahajBERT-NCC") |
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# Initialize model |
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model = AlbertForSequenceClassification.from_pretrained("neuropark/sahajBERT-NCC") |
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# Initialize pipeline |
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pipeline = TextClassificationPipeline(tokenizer=tokenizer, model=model) |
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raw_text = "এই ইউনিয়নে ৩ টি মৌজা ও ১০ টি গ্রাম আছে ।" # Change me |
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output = pipeline(raw_text) |
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``` |
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#### Limitations and bias |
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<!-- Provide examples of latent issues and potential remediations. --> |
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WIP |
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## Training data |
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The model was initialized with pre-trained weights of [sahajBERT](https://huggingface.co/neuropark/sahajBERT) at step 19519 and trained on the `sna.bn` split of [IndicGlue](https://huggingface.co/datasets/indic_glue). |
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## Training procedure |
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Coming soon! |
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<!-- ```bibtex |
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@inproceedings{..., |
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year={2020} |
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} |
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``` --> |
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## Eval results |
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Loss: 0.2477145493030548 |
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Accuracy: 0.926293408929837 |
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Macro F1: 0.9079785326650756 |
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Recall: 0.926293408929837 |
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Weighted F1: 0.9266428029354202 |
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Macro Precision: 0.9109938492260489 |
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Micro Precision: 0.926293408929837 |
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Weighted Precision: 0.9288535478995414 |
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Macro Recall: 0.9069095007692186 |
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Micro Recall: 0.926293408929837 |
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Weighted Recall: 0.926293408929837 |
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### BibTeX entry and citation info |
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Coming soon! |
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<!-- ```bibtex |
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@inproceedings{..., |
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year={2020} |
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} |
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``` --> |
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