Instructions to use nbhimte/tiny-bert-best with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nbhimte/tiny-bert-best with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nbhimte/tiny-bert-best")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nbhimte/tiny-bert-best") model = AutoModelForSequenceClassification.from_pretrained("nbhimte/tiny-bert-best", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 96ec256c17381a2549b603ad511d75160c4a6ce2341b4dda6f81a132d7461c81
- Size of remote file:
- 44.7 MB
- SHA256:
- 85b90ccb6ac53dc0f32a5b064bbd232f0cda5d3ffa89d86499460d408caa4f0f
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