Instructions to use anaghasavit/tinyBERTCrossEncoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anaghasavit/tinyBERTCrossEncoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="anaghasavit/tinyBERTCrossEncoder")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("anaghasavit/tinyBERTCrossEncoder") model = AutoModelForSequenceClassification.from_pretrained("anaghasavit/tinyBERTCrossEncoder") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d6316ccd404d859d13b13a438132769fb0b5d62ca7ab9fc367cebe5273232f9c
- Size of remote file:
- 17.6 MB
- SHA256:
- 151dfca7e0ddb083bf659785f0ce10a812f093a2c1d959681e76a1ef3e647b28
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