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 verifyToken field to verify evaluation results are produced by Hugging Face's automatic model evaluator
Beep boop, I am a bot from Hugging Face's automatic model evaluator 👋! We've added a new `verifyToken` field to your evaluation results to verify that they are produced by the model evaluator. Accept this PR to ensure that your results remain listed as **verified** on the [Hub leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards).79fae78
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Add evaluation results on the mrpc config of glue (#1)
- Add evaluation results on the mrpc config of glue (84e0cd37eda4053e63975ff318fcc4b944484493) Co-authored-by: Evaluation Bot <autoevaluator@users.noreply.huggingface.co>1b9f67b
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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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