Instructions to use VMware/bert-tiny-mrqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VMware/bert-tiny-mrqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="VMware/bert-tiny-mrqa")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("VMware/bert-tiny-mrqa") model = AutoModelForQuestionAnswering.from_pretrained("VMware/bert-tiny-mrqa") - Notebooks
- Google Colab
- Kaggle
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README.md
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- f1
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model-index:
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- name: VMware/
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results:
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- task:
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type:
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dataset:
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type: mrqa
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name:
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metrics:
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- type: exact_match
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value: 22.78
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- f1
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model-index:
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- name: VMware/TinyRoBERTa-MRQA
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results:
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- task:
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type: Question-Answering
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dataset:
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type: mrqa # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
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name: mrqa # Required. A pretty name for the dataset. Example: Common Voice (French)
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metrics:
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- type: exact_match
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value: 22.78
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