Instructions to use ModelTC/bert-base-uncased-rte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModelTC/bert-base-uncased-rte with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ModelTC/bert-base-uncased-rte")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ModelTC/bert-base-uncased-rte") model = AutoModelForSequenceClassification.from_pretrained("ModelTC/bert-base-uncased-rte", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6269e797dd1788f4e667da7daf266071b8be0d480c19869b2177b1404d936c86
- Size of remote file:
- 438 MB
- SHA256:
- 63733fb269828a14b20e371ee6ca3da62028da2831aaeaea00fe09df62e53b3a
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