Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use autoevaluate/glue-qqp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autoevaluate/glue-qqp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autoevaluate/glue-qqp", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("autoevaluate/glue-qqp") model = AutoModelForSequenceClassification.from_pretrained("autoevaluate/glue-qqp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- edc634d933fe4a1e518eae4f51e992f5ea87ab13d5c09fb314b1eee80fa9e67d
- Size of remote file:
- 268 MB
- SHA256:
- 2a9df1b612c11f18e478db67b471d3e589c3069eb1f05ecc539ebb1004fffffd
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