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
Download tokenizer.json from autoevaluate/binary-classification: direct link, hf CLI and curl.
- Browser
- Download file 711 kB
-
https://huggingface.co/autoevaluate/binary-classification/resolve/refs%2Fpr%2F98/tokenizer.json
- Command line
-
hf download hf://autoevaluate/binary-classification@refs/pr/98/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/autoevaluate/binary-classification/resolve/refs%2Fpr%2F98/tokenizer.json
711 kB
File too large to display, you can check the raw version instead.