Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use sgugger/bert-finetuned-mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sgugger/bert-finetuned-mrpc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sgugger/bert-finetuned-mrpc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sgugger/bert-finetuned-mrpc") model = AutoModelForSequenceClassification.from_pretrained("sgugger/bert-finetuned-mrpc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sgugger/bert-finetuned-mrpc: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/sgugger/bert-finetuned-mrpc/resolve/refs%2Fpr%2F3/pytorch_model.bin
- Command line
-
hf download hf://sgugger/bert-finetuned-mrpc@refs/pr/3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sgugger/bert-finetuned-mrpc/resolve/refs%2Fpr%2F3/pytorch_model.bin
433 MB
- Xet hash:
- bd31cadf29d4dafe796740a40cb63f8f22cba5622d628836f286c5771156d98b
- Size of remote file:
- 433 MB
- SHA256:
- c651fe9aa36459e8948129d64fb802e87434e850644fb5ef76cf1fe8dbc7d301
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