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--- |
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license: apache-2.0 |
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base_model: google-bert/bert-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: arg-quality-regression |
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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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# arg-quality-regression |
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This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0342 |
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- Mse: 0.0342 |
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- Mae: 0.1359 |
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- R2: 0.1353 |
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- Accuracy: 0.9808 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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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: 16 |
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- eval_batch_size: 16 |
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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: 11 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:-------:|:--------:| |
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| 0.0277 | 1.0 | 1512 | 0.0398 | 0.0398 | 0.1450 | -0.0046 | 0.9736 | |
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| 0.0218 | 2.0 | 3024 | 0.0342 | 0.0342 | 0.1359 | 0.1353 | 0.9808 | |
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| 0.0169 | 3.0 | 4536 | 0.0367 | 0.0367 | 0.1409 | 0.0717 | 0.9783 | |
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| 0.0114 | 4.0 | 6048 | 0.0400 | 0.0400 | 0.1477 | -0.0108 | 0.9751 | |
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| 0.0075 | 5.0 | 7560 | 0.0439 | 0.0439 | 0.1564 | -0.1093 | 0.9704 | |
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| 0.006 | 6.0 | 9072 | 0.0465 | 0.0465 | 0.1626 | -0.1749 | 0.9661 | |
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| 0.0051 | 7.0 | 10584 | 0.0429 | 0.0429 | 0.1574 | -0.0851 | 0.9729 | |
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| 0.0037 | 8.0 | 12096 | 0.0440 | 0.0440 | 0.1590 | -0.1123 | 0.9720 | |
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| 0.0035 | 9.0 | 13608 | 0.0412 | 0.0412 | 0.1534 | -0.0401 | 0.9755 | |
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| 0.0029 | 10.0 | 15120 | 0.0415 | 0.0415 | 0.1537 | -0.0487 | 0.9743 | |
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| 0.0028 | 11.0 | 16632 | 0.0438 | 0.0438 | 0.1589 | -0.1080 | 0.9712 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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