Instructions to use hf-internal-testing/mrpc-bert-base-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/mrpc-bert-base-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/mrpc-bert-base-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/mrpc-bert-base-cased") - Notebooks
- Google Colab
- Kaggle
Create README.md
Browse files
README.md
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---
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language: en
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license: apache-2.0
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datasets:
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- mrpc
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---
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# MRPC BERT base model (cased)
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This is a sample model fine-tuned from the [nlp_example_script.py](https://github.com/huggingface/accelerate/blob/main/examples/nlp_example.py) using Accelerate and saved at the end.
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