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:
- 3cbde52eb63177cea928157057ac8f107fe2d336af44989ddf2408628f1fa823
- Size of remote file:
- 559 Bytes
- SHA256:
- 2d06a4f92c429484a402b784b474cf62f3efa5fe9948c3f3e1abe501d528bb3f
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