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:
- 48df2ac4b1d0c49b18dcba1e47a2d6a1ad43fd2d8ad3ed1dccc9e5169846b04f
- Size of remote file:
- 559 Bytes
- SHA256:
- 02007fb0ca60e4f0f6455de0778da56d3b59c4ed865f5e8cfd53961284489359
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