Instructions to use ModelTC/bert-base-uncased-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModelTC/bert-base-uncased-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ModelTC/bert-base-uncased-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ModelTC/bert-base-uncased-mnli") model = AutoModelForSequenceClassification.from_pretrained("ModelTC/bert-base-uncased-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 728276c05d3e5be640bcd0dcf1507a0dd30d28ef127fbe78d1a32efcebb38495
- Size of remote file:
- 438 MB
- SHA256:
- 6171d37d1d3798119b2c029c2c3d69c3d3dda5c2e87cdb1b488639d2caed8c3b
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