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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: 0.8864678899082569 |
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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: 0.5142 |
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- Accuracy: 0.8865 |
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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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| 0.4686 | 0.06 | 500 | 0.4020 | 0.8337 | |
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| 0.384 | 0.12 | 1000 | 0.3666 | 0.8360 | |
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| 0.381 | 0.18 | 1500 | 0.3951 | 0.8337 | |
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| 0.3609 | 0.24 | 2000 | 0.4378 | 0.8555 | |
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| 0.3616 | 0.3 | 2500 | 0.3743 | 0.8475 | |
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| 0.3521 | 0.36 | 3000 | 0.3692 | 0.8589 | |
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| 0.3113 | 0.42 | 3500 | 0.5072 | 0.8486 | |
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| 0.319 | 0.48 | 4000 | 0.4212 | 0.8612 | |
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| 0.3034 | 0.53 | 4500 | 0.4555 | 0.8647 | |
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| 0.3098 | 0.59 | 5000 | 0.4163 | 0.8635 | |
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| 0.3113 | 0.65 | 5500 | 0.5226 | 0.8440 | |
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| 0.2949 | 0.71 | 6000 | 0.4137 | 0.875 | |
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| 0.2977 | 0.77 | 6500 | 0.4775 | 0.8486 | |
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| 0.3077 | 0.83 | 7000 | 0.4774 | 0.8693 | |
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| 0.2953 | 0.89 | 7500 | 0.4491 | 0.8589 | |
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| 0.2846 | 0.95 | 8000 | 0.5228 | 0.8784 | |
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| 0.292 | 1.01 | 8500 | 0.4801 | 0.8865 | |
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| 0.2185 | 1.07 | 9000 | 0.4889 | 0.8933 | |
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| 0.2343 | 1.13 | 9500 | 0.5862 | 0.8716 | |
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| 0.2667 | 1.19 | 10000 | 0.4796 | 0.8842 | |
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| 0.252 | 1.25 | 10500 | 0.5181 | 0.8842 | |
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| 0.2385 | 1.31 | 11000 | 0.5148 | 0.875 | |
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| 0.2144 | 1.37 | 11500 | 0.5345 | 0.8704 | |
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| 0.2348 | 1.43 | 12000 | 0.5073 | 0.8807 | |
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| 0.2166 | 1.48 | 12500 | 0.4885 | 0.8865 | |
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| 0.2104 | 1.54 | 13000 | 0.6118 | 0.8658 | |
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| 0.2145 | 1.6 | 13500 | 0.5091 | 0.8865 | |
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| 0.2098 | 1.66 | 14000 | 0.5221 | 0.8876 | |
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| 0.2111 | 1.72 | 14500 | 0.5031 | 0.8888 | |
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| 0.2042 | 1.78 | 15000 | 0.5257 | 0.8796 | |
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| 0.2091 | 1.84 | 15500 | 0.5175 | 0.8819 | |
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| 0.2027 | 1.9 | 16000 | 0.5528 | 0.8784 | |
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| 0.2173 | 1.96 | 16500 | 0.5142 | 0.8865 | |
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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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