Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JeremiahZ/bert-base-uncased-qqp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeremiahZ/bert-base-uncased-qqp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeremiahZ/bert-base-uncased-qqp")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/bert-base-uncased-qqp") model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/bert-base-uncased-qqp") - Notebooks
- Google Colab
- Kaggle
Commit History
Add verifyToken field to verify evaluation results are produced by Hugging Face's automatic model evaluator (#2) f5f4b59
Add evaluation results on the qqp config and validation split of glue (#1) 2327c12
update model card README.md 18a25db
Jeremiah Zhou commited on
update model card README.md c90e72d
Jeremiah Zhou commited on