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:
- 960a30795e8669958fe93828e4a470db724ae6f12d74ccc3375d8e5ce4f89fc9
- Size of remote file:
- 876 MB
- SHA256:
- ab18e2d0c49f71cac7a139d3beb2c3a9b820c5a4a1406fb0b6fbf1390b6f70a3
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