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:
- 91510167162f62c85d00eac0a47c593656ec5145089e5e4be835c84f80510037
- Size of remote file:
- 438 MB
- SHA256:
- f7ecc6a182477feca62817a4e028dbd3a949fac885da7b457b4517ad086774c9
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