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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b763e926aa066883ed5b58bc151f68d33bc37ad539a96cc0e92354a1d4f9dafc
- Size of remote file:
- 712 MB
- SHA256:
- 3b1931b3465666bb4b39445c91d626ffa78cbb54dcfb320af0a884da6e2edec2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.