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:
- f184e4058085377f52ba884ca815bf2a1c9c79be9507eecd7aaac8ae42f77387
- Size of remote file:
- 3.31 kB
- SHA256:
- 664ba73b52241b19454389c18b7d0c3606e7e1ea5b913955f4342bf1635df878
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