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--- |
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base_model: huawei-noah/TinyBERT_General_4L_312D |
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tags: |
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- generated_from_trainer |
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datasets: |
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- sst2 |
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metrics: |
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- accuracy |
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model-index: |
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- name: TinyBERT_SST2 |
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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: sst2 |
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type: sst2 |
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config: default |
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split: validation |
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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: 0.8795871559633027 |
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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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# TinyBERT_SST2 |
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This model is a fine-tuned version of [huawei-noah/TinyBERT_General_4L_312D](https://huggingface.co/huawei-noah/TinyBERT_General_4L_312D) on the sst2 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5289 |
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- Accuracy: 0.8796 |
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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: 8 |
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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: 2 |
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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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| 0.4655 | 0.06 | 500 | 0.4229 | 0.8234 | |
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| 0.3869 | 0.12 | 1000 | 0.3870 | 0.8383 | |
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| 0.3786 | 0.18 | 1500 | 0.4844 | 0.8234 | |
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| 0.3573 | 0.24 | 2000 | 0.4276 | 0.8532 | |
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| 0.3502 | 0.3 | 2500 | 0.4048 | 0.8372 | |
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| 0.3473 | 0.36 | 3000 | 0.3623 | 0.8658 | |
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| 0.3283 | 0.42 | 3500 | 0.4937 | 0.8681 | |
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| 0.3198 | 0.48 | 4000 | 0.4020 | 0.8532 | |
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| 0.3006 | 0.53 | 4500 | 0.4514 | 0.8612 | |
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| 0.3254 | 0.59 | 5000 | 0.4370 | 0.8624 | |
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| 0.2923 | 0.65 | 5500 | 0.5068 | 0.8544 | |
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| 0.2959 | 0.71 | 6000 | 0.4557 | 0.8704 | |
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| 0.3003 | 0.77 | 6500 | 0.4536 | 0.8647 | |
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| 0.3049 | 0.83 | 7000 | 0.4810 | 0.8704 | |
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| 0.3008 | 0.89 | 7500 | 0.4431 | 0.8681 | |
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| 0.2937 | 0.95 | 8000 | 0.5207 | 0.8693 | |
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| 0.2805 | 1.01 | 8500 | 0.4972 | 0.8784 | |
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| 0.2176 | 1.07 | 9000 | 0.5370 | 0.8773 | |
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| 0.2379 | 1.13 | 9500 | 0.5453 | 0.8807 | |
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| 0.2639 | 1.19 | 10000 | 0.5117 | 0.8693 | |
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| 0.2555 | 1.25 | 10500 | 0.6062 | 0.8670 | |
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| 0.2324 | 1.31 | 11000 | 0.5623 | 0.8704 | |
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| 0.2225 | 1.37 | 11500 | 0.5804 | 0.8773 | |
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| 0.2332 | 1.43 | 12000 | 0.5089 | 0.8807 | |
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| 0.2214 | 1.48 | 12500 | 0.5565 | 0.8796 | |
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| 0.2105 | 1.54 | 13000 | 0.5614 | 0.8739 | |
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| 0.2174 | 1.6 | 13500 | 0.5561 | 0.875 | |
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| 0.2196 | 1.66 | 14000 | 0.5165 | 0.8819 | |
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| 0.2067 | 1.72 | 14500 | 0.5249 | 0.8796 | |
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| 0.1986 | 1.78 | 15000 | 0.5121 | 0.875 | |
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| 0.2103 | 1.84 | 15500 | 0.5044 | 0.875 | |
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| 0.2115 | 1.9 | 16000 | 0.5241 | 0.8784 | |
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| 0.2011 | 1.96 | 16500 | 0.5289 | 0.8796 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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