Instructions to use DSI/TweetBasedSA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DSI/TweetBasedSA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DSI/TweetBasedSA")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DSI/TweetBasedSA") model = AutoModelForSequenceClassification.from_pretrained("DSI/TweetBasedSA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 15ff538049050ad341c2134b8447e560d0005616f6c54f867eafbc471586f07f
- Size of remote file:
- 651 MB
- SHA256:
- 18472396cfc8b3bc24dbd6c89d445f74e786f676edadcb3fc4af7a493b7c5805
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