Instructions to use ankitkupadhyay/xnli_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ankitkupadhyay/xnli_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ankitkupadhyay/xnli_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ankitkupadhyay/xnli_model") model = AutoModelForSequenceClassification.from_pretrained("ankitkupadhyay/xnli_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ankitkupadhyay/xnli_model: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/ankitkupadhyay/xnli_model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ankitkupadhyay/xnli_model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ankitkupadhyay/xnli_model/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- 14e485ee132f66f1531937da678605a659a489f86a63ffd596a9f786d904a379
- Size of remote file:
- 1.11 GB
- SHA256:
- 235000d561242faf3d77b5cdd7935c0dd5378c1edfdc5a2b78c32cfbe82699f3
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