Instructions to use franfj/SemEval23_task3_subtask3_multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use franfj/SemEval23_task3_subtask3_multi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="franfj/SemEval23_task3_subtask3_multi")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("franfj/SemEval23_task3_subtask3_multi") model = AutoModelForSequenceClassification.from_pretrained("franfj/SemEval23_task3_subtask3_multi") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 96a6c07278f4425881ee652e8008ec5fe28510f73836627bad6639d0745f8601
- Size of remote file:
- 712 MB
- SHA256:
- c3c7ea0cce214b41617d35eda4a05add8e7b80ee496f2e0b20299bc70bf1c53b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.