Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use ThirdEyeData/Text_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ThirdEyeData/Text_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ThirdEyeData/Text_Classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ThirdEyeData/Text_Classification") model = AutoModelForSequenceClassification.from_pretrained("ThirdEyeData/Text_Classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7fcbbc9ed1054ca0edab047b415bae11827395c3531ed2e8490a73e57f1b6100
- Size of remote file:
- 268 MB
- SHA256:
- aa4607fa0f2850c3ce0119e2f2c09db46bd6183ded1f4c13b2ca7ea295005db8
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