Instructions to use textattack/bert-base-uncased-snli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/bert-base-uncased-snli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/bert-base-uncased-snli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-snli") model = AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-snli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- da3276a82dd94625726d088849ad1583e6995c60f7dc9876daeade0e6e354ff3
- Size of remote file:
- 438 MB
- SHA256:
- 9e619a4b8b1bca3ad8a854a90d3cae0807154821d97154b80e2218a761a51c88
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