Instructions to use henryscheible/mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use henryscheible/mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="henryscheible/mnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("henryscheible/mnli") model = AutoModelForSequenceClassification.from_pretrained("henryscheible/mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from henryscheible/mnli: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/henryscheible/mnli/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://henryscheible/mnli/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/henryscheible/mnli/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- bf5ef72eb01cc6a8c2e2ae440cf8683208393f0e2b02610a88b10dad7fb0b003
- Size of remote file:
- 438 MB
- SHA256:
- c4037bda0451975a30e0d7ebe3cf210532d1ac66bd59bcc48f9b10772bff9c7d
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