Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
bert-base-uncased
fp32
Eval Results (legacy)
text-embeddings-inference
Instructions to use Intel/bert-base-uncased-mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Intel/bert-base-uncased-mrpc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Intel/bert-base-uncased-mrpc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Intel/bert-base-uncased-mrpc") model = AutoModelForSequenceClassification.from_pretrained("Intel/bert-base-uncased-mrpc", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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Add evaluation results on the mrpc config of glue
Beep boop, I am a bot from Hugging Face's automatic model evaluator 👋!\ Your model has been evaluated on the mrpc config of the [glue](https://huggingface.co/datasets/glue) dataset by @xinhe, using the predictions stored [here](https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-glue-mrpc-71a11b-14455978).\ Accept this pull request to see the results displayed on the [Hub leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=glue).\ Evaluate your model on more datasets [here](https://huggingface.co/spaces/autoevaluate/model-evaluator?dataset=glue).84e0cd3
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remove result.json files
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Update README.md
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upload fine-tuned model
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Update README.md
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initial commit
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