Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JeremiahZ/bert-base-uncased-rte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeremiahZ/bert-base-uncased-rte with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeremiahZ/bert-base-uncased-rte")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/bert-base-uncased-rte") model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/bert-base-uncased-rte") - Notebooks
- Google Colab
- Kaggle
Commit History
Add evaluation results on the rte config and validation split of glue (#1) 08fb8b8
update model card README.md f6cf98a
Jeremiah Zhou commited on
End of training b54260c
Jeremiah Zhou commited on
update model card README.md c7767b9
Jeremiah Zhou commited on
Model save 9bd5653
Jeremiah Zhou commited on
Training in progress, epoch 1 dda7a6e
Jeremiah Zhou commited on
initial commit 18bbded
Jeremiah Zhou commited on