Instructions to use vuiseng9/bert-base-uncased-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vuiseng9/bert-base-uncased-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vuiseng9/bert-base-uncased-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vuiseng9/bert-base-uncased-mnli") model = AutoModelForSequenceClassification.from_pretrained("vuiseng9/bert-base-uncased-mnli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eabd4c2c6cae8a216f24cd9d90597b2ef0cbca78541d87008257f94fe6730e7d
- Size of remote file:
- 438 MB
- SHA256:
- 700ad3dcd617847dffe47936516873bd6e16af283fd83ed41904c75ae3e5df09
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