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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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base_model: openai-community/gpt2 |
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model-index: |
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- name: gpt2-finetuned-mcqa-sciq |
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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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# gpt2-finetuned-mcqa-sciq |
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This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.3533 |
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- Bertscore Precision: 0.1082 |
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- Bertscore Recall: 0.1141 |
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- Bertscore F1: 0.1111 |
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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: 3e-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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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bertscore Precision | Bertscore Recall | Bertscore F1 | |
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|:-------------:|:------:|:-----:|:---------------:|:-------------------:|:----------------:|:------------:| |
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| 4.4695 | 0.9999 | 5839 | 2.3612 | 0.1082 | 0.1140 | 0.1110 | |
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| 4.0507 | 2.0 | 11679 | 2.3533 | 0.1082 | 0.1141 | 0.1111 | |
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| 3.8779 | 2.9999 | 17518 | 2.3820 | 0.1080 | 0.1140 | 0.1110 | |
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| 3.2852 | 4.0 | 23358 | 2.4208 | 0.1080 | 0.1140 | 0.1109 | |
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| 3.6416 | 4.9999 | 29197 | 2.4768 | 0.1079 | 0.1139 | 0.1108 | |
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| 2.9843 | 6.0 | 35037 | 2.5445 | 0.1079 | 0.1139 | 0.1108 | |
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| 2.8509 | 6.9999 | 40876 | 2.6094 | 0.1079 | 0.1139 | 0.1108 | |
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| 2.6932 | 8.0 | 46716 | 2.6658 | 0.1078 | 0.1138 | 0.1107 | |
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| 2.5309 | 8.9999 | 52555 | 2.7283 | 0.1078 | 0.1138 | 0.1107 | |
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| 2.5619 | 9.9991 | 58390 | 2.7585 | 0.1078 | 0.1138 | 0.1107 | |
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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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