Instructions to use balikasg/SemEval2023Task4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use balikasg/SemEval2023Task4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="balikasg/SemEval2023Task4")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("balikasg/SemEval2023Task4") model = AutoModelForSequenceClassification.from_pretrained("balikasg/SemEval2023Task4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cae7b44d7cdd4bb53e67d6ae8d0db4b3967f6f07ef71349e6b6e7f44f8d6420d
- Size of remote file:
- 6.27 GB
- SHA256:
- da582e73ad3d403d2fecea8b5709295b7823d5d097927b4c059a1117bc3ddb29
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