Instructions to use textattack/bert-base-uncased-MNLI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/bert-base-uncased-MNLI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/bert-base-uncased-MNLI")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-MNLI") model = AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-MNLI") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2c4142c410d50b4b91dc0488d045e6e790ed47f16eeaaaf12d3cba957ecd23cc
- Size of remote file:
- 438 MB
- SHA256:
- e672cb4f286f6f80c4212780771bd8beb9a61710c560d3fbebe42be662fb6780
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