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--- |
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license: apache-2.0 |
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base_model: distilbert-base-uncased-finetuned-sst-2-english |
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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: distilbert_base_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: 0.8979357798165137 |
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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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# distilbert_base_SST2 |
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This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the sst2 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4747 |
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- Accuracy: 0.8979 |
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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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| 0.1303 | 0.06 | 500 | 0.6599 | 0.8784 | |
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| 0.1781 | 0.12 | 1000 | 0.5644 | 0.8716 | |
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| 0.1856 | 0.18 | 1500 | 0.5357 | 0.8716 | |
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| 0.1833 | 0.24 | 2000 | 0.6749 | 0.8727 | |
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| 0.1887 | 0.3 | 2500 | 0.4967 | 0.8945 | |
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| 0.1873 | 0.36 | 3000 | 0.6415 | 0.8658 | |
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| 0.1787 | 0.42 | 3500 | 0.4886 | 0.9014 | |
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| 0.1979 | 0.48 | 4000 | 0.4122 | 0.8865 | |
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| 0.1662 | 0.53 | 4500 | 0.5310 | 0.8888 | |
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| 0.1718 | 0.59 | 5000 | 0.5415 | 0.8945 | |
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| 0.1808 | 0.65 | 5500 | 0.4059 | 0.8956 | |
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| 0.1666 | 0.71 | 6000 | 0.4731 | 0.8876 | |
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| 0.1762 | 0.77 | 6500 | 0.3817 | 0.8807 | |
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| 0.1782 | 0.83 | 7000 | 0.4583 | 0.8956 | |
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| 0.1739 | 0.89 | 7500 | 0.4756 | 0.8888 | |
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| 0.1715 | 0.95 | 8000 | 0.4871 | 0.8911 | |
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| 0.1682 | 1.01 | 8500 | 0.4936 | 0.8922 | |
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| 0.095 | 1.07 | 9000 | 0.4956 | 0.8899 | |
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| 0.0928 | 1.13 | 9500 | 0.6543 | 0.8716 | |
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| 0.0855 | 1.19 | 10000 | 0.5812 | 0.8956 | |
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| 0.1032 | 1.25 | 10500 | 0.6683 | 0.8716 | |
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| 0.0982 | 1.31 | 11000 | 0.6076 | 0.8842 | |
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| 0.0907 | 1.37 | 11500 | 0.5826 | 0.8956 | |
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| 0.1085 | 1.43 | 12000 | 0.4708 | 0.8922 | |
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| 0.0785 | 1.48 | 12500 | 0.5486 | 0.8956 | |
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| 0.0903 | 1.54 | 13000 | 0.6104 | 0.875 | |
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| 0.0764 | 1.6 | 13500 | 0.5576 | 0.8888 | |
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| 0.0982 | 1.66 | 14000 | 0.5447 | 0.8888 | |
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| 0.0864 | 1.72 | 14500 | 0.4833 | 0.8922 | |
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| 0.0888 | 1.78 | 15000 | 0.4737 | 0.8945 | |
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| 0.0775 | 1.84 | 15500 | 0.4818 | 0.8991 | |
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| 0.0958 | 1.9 | 16000 | 0.4674 | 0.8991 | |
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| 0.0805 | 1.96 | 16500 | 0.4747 | 0.8979 | |
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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 2.14.7 |
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- Tokenizers 0.15.0 |
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