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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 24733d669a1901d6e07e415ca0af4144508bc894b529a65c7fdfaaae3158e46d
- Size of remote file:
- 17.6 MB
- SHA256:
- ce27343cd7a1f29b9ff4bf3ceca85fc0860f75e8ccdd1737bcb4b2026012b168
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