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
File size: 129 Bytes
dda7a6e | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:664ba73b52241b19454389c18b7d0c3606e7e1ea5b913955f4342bf1635df878
size 3311
|