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:
- ecbe4acf90a3072e719e8dfee6d86da3b90d3dcc722e73fc8a1f28878a1d590c
- Size of remote file:
- 183 MB
- SHA256:
- 12a8248c9fa284f3b3618adef62d3c7eac808a5d1e74ab8024e14057640f2bc9
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