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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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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: N_bert_agnews_padding20model |
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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: 0.9481578947368421 |
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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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# N_bert_agnews_padding20model |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the ag_news dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5675 |
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- Accuracy: 0.9482 |
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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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| 0.178 | 1.0 | 7500 | 0.2016 | 0.9387 | |
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| 0.1359 | 2.0 | 15000 | 0.1994 | 0.9463 | |
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| 0.1199 | 3.0 | 22500 | 0.2296 | 0.9439 | |
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| 0.0893 | 4.0 | 30000 | 0.2822 | 0.9433 | |
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| 0.0632 | 5.0 | 37500 | 0.2953 | 0.9384 | |
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| 0.0441 | 6.0 | 45000 | 0.3583 | 0.9458 | |
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| 0.0337 | 7.0 | 52500 | 0.3966 | 0.9433 | |
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| 0.0287 | 8.0 | 60000 | 0.4296 | 0.9434 | |
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| 0.0241 | 9.0 | 67500 | 0.4442 | 0.9414 | |
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| 0.0118 | 10.0 | 75000 | 0.5066 | 0.9405 | |
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| 0.0166 | 11.0 | 82500 | 0.4644 | 0.94 | |
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| 0.0118 | 12.0 | 90000 | 0.4789 | 0.9409 | |
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| 0.0115 | 13.0 | 97500 | 0.5151 | 0.9443 | |
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| 0.0075 | 14.0 | 105000 | 0.4855 | 0.9458 | |
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| 0.007 | 15.0 | 112500 | 0.5377 | 0.9430 | |
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| 0.0058 | 16.0 | 120000 | 0.5308 | 0.9458 | |
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| 0.0024 | 17.0 | 127500 | 0.5328 | 0.9451 | |
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| 0.0014 | 18.0 | 135000 | 0.5569 | 0.9462 | |
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| 0.0023 | 19.0 | 142500 | 0.5646 | 0.9480 | |
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| 0.0019 | 20.0 | 150000 | 0.5675 | 0.9482 | |
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### Framework versions |
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- Transformers 4.33.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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