Instructions to use adityapatkar/TweeBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adityapatkar/TweeBERTa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="adityapatkar/TweeBERTa")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("adityapatkar/TweeBERTa") model = AutoModelForSequenceClassification.from_pretrained("adityapatkar/TweeBERTa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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## Acknowledgements
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This model was fine-tuned and evaluated by Aditya Patkar. The base RoBERTa model and the Sentiment140 dataset were important in developing this model.
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## Acknowledgements
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This model was fine-tuned and evaluated by Aditya Patkar. The base RoBERTa model and the Sentiment140 dataset were important in developing this model. The training notebook along with a comprehensive comparitive analysis of different models on Sentiment140 dataset can be found at https://github.com/adityapatkar/SentimentSifter.
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