Instructions to use vuiseng9/bert-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vuiseng9/bert-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vuiseng9/bert-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vuiseng9/bert-mnli") model = AutoModelForSequenceClassification.from_pretrained("vuiseng9/bert-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 195160d2088c9ec46e9fb386937449458f99e62f3538bbcd039f16db5b2e9fa0
- Size of remote file:
- 2.74 kB
- SHA256:
- 2d6f3cbe28aea9c1b0c5b889ec91bec153ace6d29b73b98f5a59d8812b2be826
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