Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use JTH/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JTH/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JTH/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JTH/results") model = AutoModelForSequenceClassification.from_pretrained("JTH/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 58e396cfebaef5f8c0f19d1408585482bf409b42a9f852df2650620daadcce66
- Size of remote file:
- 268 MB
- SHA256:
- fe16d7507b9562ba4cadc6851c0717bd4930e8c5c3f5ca991253bace2e60696e
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