Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use autoevaluate/binary-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autoevaluate/binary-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autoevaluate/binary-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("autoevaluate/binary-classification") model = AutoModelForSequenceClassification.from_pretrained("autoevaluate/binary-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 622b5b31443c03d7de6556fd0cc23fc8405cd5e39fb3324846a4e99389b57be8
- Size of remote file:
- 268 MB
- SHA256:
- e2b923224bf65e6704445e0a1edc07c21ddcf4e36dd2623449efb2201bad5065
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