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
- Xet hash:
- 2346805917f1c5ea8d01526e4943f313d9767abba1ce5991d9b567cc06465cb0
- Size of remote file:
- 438 MB
- SHA256:
- 312ed0e40e3af6421025b6669752d6301142cf711580b41768b7a581ac3d71df
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