Instructions to use tmnam20/gpt1_n-layer-2_xnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tmnam20/gpt1_n-layer-2_xnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tmnam20/gpt1_n-layer-2_xnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tmnam20/gpt1_n-layer-2_xnli") model = AutoModelForSequenceClassification.from_pretrained("tmnam20/gpt1_n-layer-2_xnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6eff8680cda2d01307dd9a1a9b195c7fde22640ce719ecac76e3d2e034fc2390
- Size of remote file:
- 365 MB
- SHA256:
- f7273c7970b0841fc09ba6c531b24bf84fb5c85c1a33a0a6386a8fdb0a7ef210
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