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:
- ee9c686bf5f684c7e4cf6909a2339e5ab3b3f89dab7e6984996d4a260bb5184d
- Size of remote file:
- 3.9 kB
- SHA256:
- 946e99d0ac43b1464ed231b7fb72640062bc529bfa0e839bbaad3bd3dd4d1055
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