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
- Xet hash:
- 040ca4e582ff56fbe3caa87e2adf18bd52cfad74d258b1abbd1c0d41df5972f5
- Size of remote file:
- 3.31 kB
- SHA256:
- 0baa304779569562f5b2bb36707fb53551b57cf2202c1a507679287ebcdedbca
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