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 pytorch_model.bin from textattack/roberta-base-MNLI: direct link, hf CLI and curl.
- Browser
- Download file 501 MB
-
https://huggingface.co/textattack/roberta-base-MNLI/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://textattack/roberta-base-MNLI/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/textattack/roberta-base-MNLI/resolve/main/pytorch_model.bin
501 MB
- Xet hash:
- 44076f664a9c1892e837e0340cb425dc8f464e0431bfbe8d97e2fdcd91853d7e
- Size of remote file:
- 501 MB
- SHA256:
- 8791589bf3dd2025eff17f635e43bec0b842a1f3a35f2f47aa73afc8c178e0e9
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