Instructions to use tkuye/tiny-bert-jdc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tkuye/tiny-bert-jdc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tkuye/tiny-bert-jdc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tkuye/tiny-bert-jdc") model = AutoModelForSequenceClassification.from_pretrained("tkuye/tiny-bert-jdc") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1c3e7e0f141a9994d53a6fb610aa63f48dceca1235c3887f3331b408f915c02e
- Size of remote file:
- 17.6 MB
- SHA256:
- 81f169ec9375ada38d7ceecfa633cdbeece57a362b1fd9e515a2bd97cf4f998e
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