Instructions to use mtreviso/roberta-base-snli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mtreviso/roberta-base-snli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mtreviso/roberta-base-snli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mtreviso/roberta-base-snli") model = AutoModelForSequenceClassification.from_pretrained("mtreviso/roberta-base-snli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1870653103bf6f09dee40eef01451e5ec6c2c52a40b3ae49b53c3b8026952c4f
- Size of remote file:
- 499 MB
- SHA256:
- 93e5e0c28b7a558ff71fd13ecc5bef507a571cbbdea47581841195e40a3c07a7
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