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") - Notebooks
- Google Colab
- Kaggle
Add evaluation results on the sst2 config and validation split of glue
Browse filesBeep boop, I am a bot from Hugging Face's automatic model evaluator 👋!\
Your model has been evaluated on the sst2 config and validation split of the [glue](https://huggingface.co/datasets/glue) dataset by @lewtun , using the predictions stored [here](https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-ecaf0dbc-43a3-4513-bbcf-d0f372522232-109106).\
Accept this pull request to see the results displayed on the [Hub leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=glue).\
Evaluate your model on more datasets [here](https://huggingface.co/spaces/autoevaluate/model-evaluator?dataset=glue).