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--- |
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license: mit |
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base_model: gpt2 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: SentimentT2_GPT2 |
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results: [] |
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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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# SentimentT2_GPT2 |
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This model is a fine-tuned version of [gpt2](https://huggingface.co./gpt2) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0308 |
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- Accuracy: 0.8644 |
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- F1: 0.8685 |
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- Auc Roc: 0.9297 |
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- Log Loss: 1.0307 |
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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: 1 |
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- eval_batch_size: 1 |
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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: 100 |
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- num_epochs: 4 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Auc Roc | Log Loss | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:-------:|:--------:| |
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| 1.1785 | 0.15 | 500 | 0.7334 | 0.8346 | 0.8400 | 0.9144 | 0.7334 | |
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| 1.1409 | 0.31 | 1000 | 0.8797 | 0.8520 | 0.8649 | 0.9269 | 0.8796 | |
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| 1.0906 | 0.46 | 1500 | 0.7869 | 0.8744 | 0.8805 | 0.9394 | 0.7869 | |
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| 1.0163 | 0.62 | 2000 | 0.8381 | 0.8706 | 0.8771 | 0.9366 | 0.8381 | |
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| 1.0602 | 0.77 | 2500 | 0.9904 | 0.8458 | 0.8616 | 0.9253 | 0.9904 | |
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| 1.1456 | 0.93 | 3000 | 0.8833 | 0.8483 | 0.8452 | 0.9275 | 0.8832 | |
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| 0.9662 | 1.08 | 3500 | 0.9737 | 0.8507 | 0.8618 | 0.9354 | 0.9737 | |
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| 0.8496 | 1.24 | 4000 | 0.9361 | 0.8619 | 0.8680 | 0.9351 | 0.9361 | |
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| 0.8571 | 1.39 | 4500 | 0.8660 | 0.8619 | 0.8702 | 0.9346 | 0.8660 | |
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| 0.7506 | 1.55 | 5000 | 0.9359 | 0.8507 | 0.8558 | 0.9316 | 0.9359 | |
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| 0.8236 | 1.7 | 5500 | 1.1721 | 0.8184 | 0.8433 | 0.9229 | 1.1721 | |
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| 0.6897 | 1.85 | 6000 | 0.9876 | 0.8532 | 0.8547 | 0.9318 | 0.9876 | |
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| 0.6699 | 2.01 | 6500 | 0.8947 | 0.8570 | 0.8671 | 0.9323 | 0.8946 | |
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| 0.6137 | 2.16 | 7000 | 0.9318 | 0.8557 | 0.8661 | 0.9344 | 0.9318 | |
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| 0.4646 | 2.32 | 7500 | 0.9943 | 0.8595 | 0.8660 | 0.9312 | 0.9944 | |
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| 0.7042 | 2.47 | 8000 | 0.9150 | 0.8657 | 0.8714 | 0.9345 | 0.9150 | |
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| 0.4079 | 2.63 | 8500 | 1.0215 | 0.8657 | 0.8750 | 0.9312 | 1.0214 | |
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| 0.4646 | 2.78 | 9000 | 0.9809 | 0.8619 | 0.8714 | 0.9310 | 0.9809 | |
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| 0.4707 | 2.94 | 9500 | 1.0151 | 0.8644 | 0.8719 | 0.9279 | 1.0150 | |
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| 0.5005 | 3.09 | 10000 | 1.0748 | 0.8607 | 0.8651 | 0.9289 | 1.0747 | |
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| 0.3817 | 3.24 | 10500 | 0.8819 | 0.8781 | 0.8858 | 0.9299 | 0.8818 | |
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| 0.279 | 3.4 | 11000 | 1.0542 | 0.8607 | 0.8627 | 0.9302 | 1.0541 | |
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| 0.3527 | 3.55 | 11500 | 1.0148 | 0.8607 | 0.8637 | 0.9312 | 1.0147 | |
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| 0.3873 | 3.71 | 12000 | 1.0421 | 0.8619 | 0.8648 | 0.9294 | 1.0420 | |
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| 0.3552 | 3.86 | 12500 | 1.0308 | 0.8644 | 0.8685 | 0.9297 | 1.0307 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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