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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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- Multiple Choice |
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metrics: |
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- accuracy |
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model-index: |
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- name: bert-base-uncased-e_CARE |
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results: [] |
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datasets: |
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- 12ml/e-CARE |
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language: |
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- en |
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pipeline_tag: question-answering |
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--- |
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# bert-base-uncased-e_CARE |
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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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It achieves the following results on the evaluation set: |
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- Loss: 1.7677 |
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- Accuracy: 0.7212 |
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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/Multiple%20Choice/e-CARE/e_CARE_Multiple_Choice_Using_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/12ml/e-CARE |
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**Histogram of Input 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: 5e-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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.5637 | 1.0 | 1571 | 0.5282 | 0.7244 | |
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| 0.345 | 2.0 | 3142 | 0.6667 | 0.7320 | |
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| 0.1098 | 3.0 | 4713 | 1.3113 | 0.7257 | |
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| 0.0212 | 4.0 | 6284 | 1.8194 | 0.7225 | |
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| 0.0185 | 5.0 | 7855 | 1.7677 | 0.7212 | |
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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 |
