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:
- 89f06befabde1c1e022bf805944779c503c84f13fe6930c1fe67067adaf89ddc
- Size of remote file:
- 14.6 kB
- SHA256:
- 671b5daa61c20b74881e58dd253b4c24aaf7e135f30f7413c18cb88b09bcd43a
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