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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: distilgpt2 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: distilgpt2-finetuned |
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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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# distilgpt2-finetuned |
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This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.6391 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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: 1 |
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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 | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 4.0748 | 0.0436 | 50 | 3.8923 | |
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| 3.8414 | 0.0871 | 100 | 3.8125 | |
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| 3.8957 | 0.1307 | 150 | 3.7769 | |
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| 3.8723 | 0.1743 | 200 | 3.7545 | |
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| 4.0205 | 0.2179 | 250 | 3.7336 | |
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| 3.7175 | 0.2614 | 300 | 3.7282 | |
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| 3.7778 | 0.3050 | 350 | 3.7111 | |
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| 3.7763 | 0.3486 | 400 | 3.6994 | |
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| 3.8142 | 0.3922 | 450 | 3.6945 | |
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| 3.7654 | 0.4357 | 500 | 3.6831 | |
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| 3.9636 | 0.4793 | 550 | 3.6773 | |
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| 3.703 | 0.5229 | 600 | 3.6692 | |
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| 3.6114 | 0.5664 | 650 | 3.6647 | |
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| 3.6269 | 0.6100 | 700 | 3.6591 | |
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| 3.693 | 0.6536 | 750 | 3.6564 | |
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| 3.7969 | 0.6972 | 800 | 3.6529 | |
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| 3.6011 | 0.7407 | 850 | 3.6491 | |
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| 3.4943 | 0.7843 | 900 | 3.6466 | |
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| 3.7543 | 0.8279 | 950 | 3.6440 | |
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| 3.861 | 0.8715 | 1000 | 3.6406 | |
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| 3.5354 | 0.9150 | 1050 | 3.6401 | |
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| 3.6661 | 0.9586 | 1100 | 3.6396 | |
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### Framework versions |
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- Transformers 4.45.1 |
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- Pytorch 2.4.0 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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