Instructions to use tkuye/tiny-jdc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tkuye/tiny-jdc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tkuye/tiny-jdc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tkuye/tiny-jdc") model = AutoModelForSequenceClassification.from_pretrained("tkuye/tiny-jdc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f68a5e24cda10edad0a96a682879a04f9438eff0c6484882772a48291287afe8
- Size of remote file:
- 361 kB
- SHA256:
- 6d1b6417e01f89cf9b6ab60eb840d475f73aad6cc2e3b826d7200cc258e429fa
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