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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 554a02a87fd76ee0354eac2af24bb1a84c4a5d5f262f1a998e612eecc31cc6bd
- Size of remote file:
- 380 kB
- SHA256:
- b6500503ad09a01d744240754f9d7fbad714d14a6fc38785f5eb64d962ce0a6d
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