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---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-QnA-MLQA_Dataset
results: []
datasets:
- mlqa
language:
- en
metrics:
- exact_match
- f1
---
# bert-base-uncased-QnA-MLQA_Dataset
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased).
## Model description
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
## Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
## Training and evaluation data
Dataset Source: https://huggingface.co/datasets/mlqa/viewer/mlqa.en.en/test
__Histogram of Input (Both Context & Question) Lengths__
__Histogram of Context Lengths__
__Histogram of Question Lengths__
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Metric Name | Metric Value |
|:-----:|:-----:|
| Exact Match | 59.6146 |
| F1 | 73.3002 |
* All values in the above chart are rounded to the nearest ten-thousandth.
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3 |