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--- |
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language: |
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- en |
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license: mit |
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base_model: microsoft/deberta-v3-large |
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tags: |
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- generated_from_trainer |
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datasets: |
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- glue |
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metrics: |
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- matthews_correlation |
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model-index: |
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- name: output |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: GLUE COLA |
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type: glue |
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config: cola |
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split: validation |
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args: cola |
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metrics: |
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- name: Matthews Correlation |
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type: matthews_correlation |
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value: 0.7060783174788182 |
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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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# output |
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This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the GLUE COLA dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3123 |
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- Matthews Correlation: 0.7061 |
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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: 1e-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: 10.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |
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|:-------------:|:-----:|:----:|:---------------:|:--------------------:| |
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| 0.3546 | 1.0 | 535 | 0.3123 | 0.7061 | |
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| 0.2078 | 2.0 | 1070 | 0.3618 | 0.7311 | |
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| 0.1313 | 3.0 | 1605 | 0.5145 | 0.7160 | |
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| 0.087 | 4.0 | 2140 | 0.5819 | 0.7230 | |
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| 0.0597 | 5.0 | 2675 | 0.6325 | 0.7397 | |
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| 0.0435 | 6.0 | 3210 | 0.6152 | 0.7332 | |
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| 0.0268 | 7.0 | 3745 | 0.7296 | 0.7327 | |
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| 0.0304 | 8.0 | 4280 | 0.7672 | 0.7287 | |
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| 0.015 | 9.0 | 4815 | 0.8067 | 0.7264 | |
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| 0.0133 | 10.0 | 5350 | 0.8079 | 0.7246 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.0.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.15.0 |
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