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--- |
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license: apache-2.0 |
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base_model: bert-base-uncased |
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tags: |
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- generated_from_trainer |
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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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metrics: |
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- exact_match |
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- f1 |
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--- |
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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 hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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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 |
