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")# 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:
- 1488dc75b0ffbe76b80f850a8da5e7e18e275e81e3eb882e3daba2d85ef7eab4
- Size of remote file:
- 3.31 kB
- SHA256:
- c5b06bbfc79f974609c4b670603b365530b5d295dd538d5c7b2dd7ccdc19eb53
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.