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
Librarian Bot: Add base_model information to model (#3) ab35e43
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
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End of training 87f8056
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update model card README.md c90e72d
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Model save f2c1e0b
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Training in progress, epoch 3 6615eb6
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Training in progress, epoch 2 9086508
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Training in progress, epoch 1 acfa0b4
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initial commit eb3968c
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