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:
- 55fd20746556e19c2e02e1b193520994d2eaf30419fdd09cd4a97afff42a1145
- Size of remote file:
- 438 MB
- SHA256:
- d87744f0ab65a46048ed3f167605ca38bf1e838b61af3d305013a2e46cbb6032
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