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--- |
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license: apache-2.0 |
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base_model: distilbert/distilgpt2 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- eli5_category |
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metrics: |
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- bleu |
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model-index: |
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- name: distilgpt2-finetuned |
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results: |
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- task: |
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name: Causal Language Modeling |
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type: text-generation |
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dataset: |
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name: eli5_category |
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type: eli5_category |
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config: default |
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split: None |
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args: default |
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metrics: |
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- name: Bleu |
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type: bleu |
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value: 0.010587533155110318 |
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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 [distilbert/distilgpt2](https://huggingface.co/distilbert/distilgpt2) on the eli5_category dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.7703 |
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- Bleu: 0.0106 |
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- Bertscore Precision: 0.1609 |
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- Bertscore Recall: 0.1758 |
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- Bertscore F1: 0.1677 |
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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: 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: 3.0 |
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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 | Bleu | Bertscore Precision | Bertscore Recall | Bertscore F1 | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------------------:|:----------------:|:------------:| |
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| 3.8816 | 1.0 | 4000 | 3.7775 | 0.0107 | 0.1607 | 0.1756 | 0.1675 | |
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| 3.7273 | 2.0 | 8000 | 3.7660 | 0.0107 | 0.1608 | 0.1757 | 0.1676 | |
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| 3.6125 | 3.0 | 12000 | 3.7703 | 0.0106 | 0.1609 | 0.1758 | 0.1677 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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