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--- |
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license: mit |
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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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- f1 |
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model-index: |
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- name: debertav3-finetuned-answer-polarity-2e6-newdata |
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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 |
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type: glue |
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config: answer_pol |
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split: validation |
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args: answer_pol |
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metrics: |
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- name: F1 |
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type: f1 |
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value: 0.8526968320709598 |
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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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# debertav3-finetuned-answer-polarity-2e6-newdata |
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This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the glue dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4009 |
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- F1: 0.8527 |
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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-06 |
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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: 6 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| No log | 1.0 | 221 | 1.1820 | 0.2111 | |
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| 1.1195 | 2.0 | 442 | 0.7073 | 0.7068 | |
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| 0.4953 | 3.0 | 663 | 0.5068 | 0.8311 | |
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| 0.4953 | 4.0 | 884 | 0.4326 | 0.8498 | |
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| 0.2767 | 5.0 | 1105 | 0.4155 | 0.8553 | |
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| 0.2147 | 6.0 | 1326 | 0.4009 | 0.8527 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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