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--- |
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license: mit |
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base_model: roberta-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- ag_news |
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metrics: |
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- accuracy |
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model-index: |
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- name: roberta_agnews_padding10model |
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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: ag_news |
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type: ag_news |
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config: default |
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split: test |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9502631578947368 |
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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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# roberta_agnews_padding10model |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the ag_news dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5337 |
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- Accuracy: 0.9503 |
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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: 20 |
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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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| 0.1966 | 1.0 | 7500 | 0.2068 | 0.9404 | |
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| 0.1632 | 2.0 | 15000 | 0.1954 | 0.9457 | |
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| 0.1432 | 3.0 | 22500 | 0.2422 | 0.9478 | |
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| 0.1223 | 4.0 | 30000 | 0.2275 | 0.9486 | |
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| 0.0994 | 5.0 | 37500 | 0.2442 | 0.9486 | |
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| 0.079 | 6.0 | 45000 | 0.3053 | 0.9486 | |
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| 0.0759 | 7.0 | 52500 | 0.3104 | 0.9463 | |
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| 0.0506 | 8.0 | 60000 | 0.3757 | 0.9472 | |
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| 0.0436 | 9.0 | 67500 | 0.3468 | 0.9470 | |
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| 0.025 | 10.0 | 75000 | 0.4170 | 0.9468 | |
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| 0.0303 | 11.0 | 82500 | 0.4168 | 0.9462 | |
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| 0.0273 | 12.0 | 90000 | 0.4173 | 0.9486 | |
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| 0.024 | 13.0 | 97500 | 0.4305 | 0.9476 | |
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| 0.0139 | 14.0 | 105000 | 0.4549 | 0.9480 | |
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| 0.0111 | 15.0 | 112500 | 0.4961 | 0.9483 | |
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| 0.0102 | 16.0 | 120000 | 0.4733 | 0.9488 | |
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| 0.0036 | 17.0 | 127500 | 0.5044 | 0.9493 | |
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| 0.0025 | 18.0 | 135000 | 0.5070 | 0.95 | |
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| 0.0024 | 19.0 | 142500 | 0.5196 | 0.9508 | |
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| 0.0018 | 20.0 | 150000 | 0.5337 | 0.9503 | |
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### Framework versions |
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- Transformers 4.32.1 |
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- Pytorch 2.1.1 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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