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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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- sibyl |
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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: bert-base-uncased-ag_news |
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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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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9375 |
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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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# bert-base-uncased-ag_news |
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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: 0.3284 |
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- Accuracy: 0.9375 |
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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: 5e-05 |
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- train_batch_size: 8 |
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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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- lr_scheduler_warmup_steps: 7425 |
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- training_steps: 74250 |
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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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| 0.5773 | 0.13 | 2000 | 0.3627 | 0.8875 | |
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| 0.3101 | 0.27 | 4000 | 0.2938 | 0.9208 | |
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| 0.3076 | 0.4 | 6000 | 0.3114 | 0.9092 | |
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| 0.3114 | 0.54 | 8000 | 0.4545 | 0.9008 | |
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| 0.3154 | 0.67 | 10000 | 0.3875 | 0.9083 | |
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| 0.3095 | 0.81 | 12000 | 0.3390 | 0.9142 | |
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| 0.2948 | 0.94 | 14000 | 0.3341 | 0.9133 | |
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| 0.2557 | 1.08 | 16000 | 0.4573 | 0.9092 | |
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| 0.258 | 1.21 | 18000 | 0.3356 | 0.9217 | |
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| 0.2455 | 1.35 | 20000 | 0.3348 | 0.9283 | |
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| 0.2361 | 1.48 | 22000 | 0.3218 | 0.93 | |
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| 0.254 | 1.62 | 24000 | 0.3814 | 0.9033 | |
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| 0.2528 | 1.75 | 26000 | 0.3628 | 0.9158 | |
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| 0.2282 | 1.89 | 28000 | 0.3302 | 0.9308 | |
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| 0.224 | 2.02 | 30000 | 0.3967 | 0.9225 | |
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| 0.174 | 2.15 | 32000 | 0.3669 | 0.9333 | |
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| 0.1848 | 2.29 | 34000 | 0.3435 | 0.9283 | |
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| 0.19 | 2.42 | 36000 | 0.3552 | 0.93 | |
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| 0.1865 | 2.56 | 38000 | 0.3996 | 0.9258 | |
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| 0.1877 | 2.69 | 40000 | 0.3749 | 0.9258 | |
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| 0.1951 | 2.83 | 42000 | 0.3963 | 0.9258 | |
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| 0.1702 | 2.96 | 44000 | 0.3655 | 0.9317 | |
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| 0.1488 | 3.1 | 46000 | 0.3942 | 0.9292 | |
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| 0.1231 | 3.23 | 48000 | 0.3998 | 0.9267 | |
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| 0.1319 | 3.37 | 50000 | 0.4292 | 0.9242 | |
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| 0.1334 | 3.5 | 52000 | 0.4904 | 0.9192 | |
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### Framework versions |
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- Transformers 4.10.2 |
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- Pytorch 1.7.1 |
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- Datasets 1.6.1 |
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- Tokenizers 0.10.3 |
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