Instructions to use EstrixDS/XLNet_SemEval_Task1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EstrixDS/XLNet_SemEval_Task1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EstrixDS/XLNet_SemEval_Task1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("EstrixDS/XLNet_SemEval_Task1") model = AutoModelForSequenceClassification.from_pretrained("EstrixDS/XLNet_SemEval_Task1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7d90edfa4e2a212b5c152a3866702b62e3287f47d0a841170661e445fa88dd37
- Size of remote file:
- 1.45 GB
- SHA256:
- 96619243563173e9d78d6306352eed2170a564c6db378c5e541b43d8d333215b
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