Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use henryscheible/mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use henryscheible/mrpc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="henryscheible/mrpc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("henryscheible/mrpc") model = AutoModelForSequenceClassification.from_pretrained("henryscheible/mrpc") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7789229432bf560cce53494e04da5c5b8e653ec627a1118d28005e9296f54d8f
- Size of remote file:
- 438 MB
- SHA256:
- 43c4108659e75bf12b86ba565c91f9f041764886b4b1cec795a619a3de17e717
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