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:
- aa7a4333a91afd8a40df9a827e5f95ce2c9ea14e173670d9211ce330dc5aebb8
- Size of remote file:
- 365 MB
- SHA256:
- 9026d72af7f2564e5c1ccd406861cdca0ac1a64029ed37875a446ac011ac0ed7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.