Instructions to use Shushant/NepaliCovidTweetsClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shushant/NepaliCovidTweetsClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Shushant/NepaliCovidTweetsClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Shushant/NepaliCovidTweetsClassification") model = AutoModelForSequenceClassification.from_pretrained("Shushant/NepaliCovidTweetsClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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# Nepali Covid Tweet Classification
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## This model was developed by finetuning the NepaliBERT model previously developed by me on Nepali COVID-related tweets. This dataset has about 15000 observations annotated with positive, negative, and neutral labels. NepaliBERT model was able to achieve SOTA results while finetuning this model for text classification. While training the model, the evaluation metrics obtained were:
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Training loss: 0.35592623149202174
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Validation loss: 0.6570735214928906
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F1 Score (Weighted): 0.7719232825307907
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# Nepali Covid Tweet Classification
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## This model was developed by finetuning the NepaliBERT model previously developed by me on Nepali COVID-related tweets. This dataset has about 15000 observations annotated with positive, negative, and neutral labels. NepaliBERT model was able to achieve SOTA results while finetuning this model for text classification. While training the model, the evaluation metrics obtained were:
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Training loss: 0.35592623149202174
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Validation loss: 0.6570735214928906
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F1 Score (Weighted): 0.7719232825307907
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