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README.md
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print("\n" + "-" * 50)
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## Citation
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If you use this model, please cite the following paper:
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print("\n" + "-" * 50)
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```
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## Benchmarks
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| | SC | EC | DC | NER | NLI |
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|-------------|--------|-------|-------|----------|----------|
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|`Metrics` | `Accuracy` | `F1*` | `Accuracy` | `F1 (Entity)*` | `Accuracy` |
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|[mBERT](https://huggingface.co/bert-base-multilingual-cased) | 83.39 | 56.02 | 98.64 | 67.40 | 75.40 |
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|[XLM-R](https://huggingface.co/xlm-roberta-base) | 89.49 | 66.70 | 98.71 | 70.63 | 76.87 |
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|[sagorsarker/bangla-bert-base](https://huggingface.co/sagorsarker/bangla-bert-base) | 87.30 | 61.51 | 98.79 | 70.97 | 70.48 |
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[monsoon-nlp/bangla-electra](https://huggingface.co/monsoon-nlp/bangla-electra) | 73.54 | 34.55 | 97.64 | 52.57 | 63.48 |
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|***BanglaBERT*** | **92.18** | **74.27** | **99.07** | **72.18** | **82.94**|
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`*` - Weighted Average
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The benchmarking datasets are as follows:
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* **SC:** **[Sentiment Classification](https://ieeexplore.ieee.org/document/8554396/)**
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* **EC:** **[Emotion Classification](https://aclanthology.org/2021.naacl-srw.19/)**
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* **DC:** **[Document Classification](https://arxiv.org/abs/2005.00085)**
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* **NER:** **[Named Entity Recognition](https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs179349)**
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* **NLI:** **[Natural Language Inference](#datasets)**
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## Citation
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If you use this model, please cite the following paper:
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