Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JeremiahZ/bert-base-uncased-mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeremiahZ/bert-base-uncased-mrpc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeremiahZ/bert-base-uncased-mrpc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/bert-base-uncased-mrpc") model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/bert-base-uncased-mrpc") - Notebooks
- Google Colab
- Kaggle
Commit History
Add evaluation results on the mrpc config and validation split of glue (#1) 47ac4af
update model card README.md 65cd154
Jeremiah Zhou commited on
End of training 08705fe
Jeremiah Zhou commited on
update model card README.md 0804d9d
Jeremiah Zhou commited on
Model save ba4fcc1
Jeremiah Zhou commited on
Training in progress, epoch 5 9cda491
Jeremiah Zhou commited on
Training in progress, epoch 1 3258a08
Jeremiah Zhou commited on
initial commit 1dcea01
Jeremiah Zhou commited on