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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: distilbert/distilbert-base-cased |
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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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- rouge |
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model-index: |
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- name: ae1c37d368ad2fed8b41ece4ad33780c |
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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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# ae1c37d368ad2fed8b41ece4ad33780c |
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This model is a fine-tuned version of [distilbert/distilbert-base-cased](https://huggingface.co/distilbert/distilbert-base-cased) on the fancyzhx/dbpedia_14 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0716 |
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- Data Size: 1.0 |
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- Epoch Runtime: 478.8393 |
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- Accuracy: 0.9897 |
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- F1 Macro: 0.9897 |
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- Rouge1: 0.9897 |
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- Rouge2: 0.0 |
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- Rougel: 0.9897 |
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- Rougelsum: 0.9897 |
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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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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- total_train_batch_size: 32 |
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- total_eval_batch_size: 32 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: constant |
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- num_epochs: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Data Size | Epoch Runtime | Accuracy | F1 Macro | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:------:|:---------------:|:---------:|:-------------:|:--------:|:--------:|:------:|:------:|:------:|:---------:| |
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| No log | 0 | 0 | 2.6453 | 0 | 18.5299 | 0.0732 | 0.0159 | 0.0732 | 0.0 | 0.0732 | 0.0731 | |
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| 0.145 | 1 | 17500 | 0.0701 | 0.0078 | 23.3785 | 0.9856 | 0.9856 | 0.9856 | 0.0 | 0.9856 | 0.9856 | |
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| 0.049 | 2 | 35000 | 0.0626 | 0.0156 | 25.6617 | 0.9866 | 0.9867 | 0.9867 | 0.0 | 0.9866 | 0.9866 | |
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| 0.0387 | 3 | 52500 | 0.0654 | 0.0312 | 32.4196 | 0.9852 | 0.9852 | 0.9853 | 0.0 | 0.9853 | 0.9852 | |
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| 0.0563 | 4 | 70000 | 0.0542 | 0.0625 | 46.2025 | 0.9884 | 0.9884 | 0.9885 | 0.0 | 0.9884 | 0.9885 | |
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| 0.0534 | 5 | 87500 | 0.0503 | 0.125 | 75.2248 | 0.9895 | 0.9895 | 0.9896 | 0.0 | 0.9895 | 0.9895 | |
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| 0.0621 | 6 | 105000 | 0.0466 | 0.25 | 130.3518 | 0.9899 | 0.9899 | 0.9899 | 0.0 | 0.9899 | 0.9899 | |
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| 0.0003 | 7 | 122500 | 0.0429 | 0.5 | 236.9868 | 0.9905 | 0.9905 | 0.9905 | 0.0 | 0.9905 | 0.9905 | |
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| 0.0296 | 8.0 | 140000 | 0.0513 | 1.0 | 482.3228 | 0.9909 | 0.9909 | 0.9909 | 0.0 | 0.9909 | 0.9908 | |
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| 0.0122 | 9.0 | 157500 | 0.0642 | 1.0 | 469.8011 | 0.9890 | 0.9890 | 0.9890 | 0.0 | 0.9890 | 0.9890 | |
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| 0.0217 | 10.0 | 175000 | 0.0645 | 1.0 | 488.3664 | 0.9898 | 0.9898 | 0.9898 | 0.0 | 0.9898 | 0.9898 | |
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| 0.0255 | 11.0 | 192500 | 0.0716 | 1.0 | 478.8393 | 0.9897 | 0.9897 | 0.9897 | 0.0 | 0.9897 | 0.9897 | |
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### Framework versions |
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- Transformers 4.57.0 |
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- Pytorch 2.8.0+cu128 |
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- Datasets 4.3.0 |
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- Tokenizers 0.22.1 |
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