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README.md
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model-index:
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- name: bert-base-uncased-QnA-MLQA_Dataset
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# bert-base-uncased-QnA-MLQA_Dataset
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased)
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## Model description
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## Intended uses & limitations
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## Training and evaluation data
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## Training procedure
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### Training results
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### Framework versions
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- Transformers 4.31.0
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.2
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- Tokenizers 0.13.3
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model-index:
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- name: bert-base-uncased-QnA-MLQA_Dataset
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results: []
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datasets:
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- mlqa
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language:
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- en
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# bert-base-uncased-QnA-MLQA_Dataset
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased).
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## Model description
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For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Question%26Answer/ML%20QA/ML_QA_Question%26Answer_with_BERT.ipynb
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## Intended uses & limitations
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This model is intended to demonstrate my ability to solve a complex problem using technology.
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## Training and evaluation data
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Dataset Source: https://huggingface.co/datasets/mlqa/viewer/mlqa.en.en/test
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__Histogram of Input (Both Context & Question) Lengths__
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__Histogram of Context Lengths__
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__Histogram of Question Lengths__
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## Training procedure
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### Training results
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| Metric Name | Metric Value |
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|:-----:|:-----:|
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| Exact Match | 59.6146 |
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| F1 | 73.3002 |
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* All values in the above chart are rounded to the nearest ten-thousandth.
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### Framework versions
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- Transformers 4.31.0
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.2
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- Tokenizers 0.13.3
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