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:
- de4e819b67c7a128a252d75cbde389c2463a224c2710ae441dbdc3bededf1b62
- Size of remote file:
- 17.6 MB
- SHA256:
- 38e7bcc0fb78e7d98e1c27211a25b775005b414ef805da9b1aae0f5ba31933f8
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