Instructions to use textattack/roberta-base-MNLI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/roberta-base-MNLI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/roberta-base-MNLI")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/roberta-base-MNLI") model = AutoModelForSequenceClassification.from_pretrained("textattack/roberta-base-MNLI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from textattack/roberta-base-MNLI: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/textattack/roberta-base-MNLI/resolve/refs%2Fpr%2F1/flax_model.msgpack
- Command line
-
hf download hf://textattack/roberta-base-MNLI@refs/pr/1/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/textattack/roberta-base-MNLI/resolve/refs%2Fpr%2F1/flax_model.msgpack
499 MB
- Xet hash:
- 1d727e847ebff1ddfd546dce419728642aabeb2bc97e3fb89a7a970e30f680ff
- Size of remote file:
- 499 MB
- SHA256:
- e1ec3edcdf16602cba75e79929b888fa01c8170f38fe9bdf109d41ae0cb2adea
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