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:
- 5a3295147e98439ce6c0163561bf87b422f7da0293c0b21fe55eb103eca80a7a
- Size of remote file:
- 3.12 kB
- SHA256:
- e675fa0fd849e7b20bc73d2c4c01df18b662d01c70352f276a680d6caf71e10b
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