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--- |
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library_name: transformers |
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license: mit |
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base_model: facebook/bart-large-cnn |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: BART-Large-psychological-dataset |
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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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# BART-Large-psychological-dataset |
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This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co./facebook/bart-large-cnn) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1549 |
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- Rouge1: 0.6621 |
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- Rouge2: 0.4488 |
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- Rougel: 0.5658 |
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- Rougelsum: 0.5656 |
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- Gen Len: 80.6204 |
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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: 8 |
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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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
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| No log | 1.0 | 274 | 0.8651 | 0.6252 | 0.3953 | 0.5206 | 0.5206 | 86.9982 | |
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| 0.7484 | 2.0 | 548 | 0.8332 | 0.648 | 0.4301 | 0.554 | 0.5541 | 79.885 | |
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| 0.7484 | 3.0 | 822 | 0.8943 | 0.6498 | 0.4335 | 0.5514 | 0.5518 | 82.635 | |
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| 0.3207 | 4.0 | 1096 | 0.9653 | 0.6571 | 0.4422 | 0.5607 | 0.5609 | 79.9708 | |
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| 0.3207 | 5.0 | 1370 | 1.0514 | 0.6582 | 0.4445 | 0.5637 | 0.5639 | 79.8047 | |
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| 0.1557 | 6.0 | 1644 | 1.0752 | 0.6607 | 0.4476 | 0.5659 | 0.5657 | 79.6058 | |
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| 0.1557 | 7.0 | 1918 | 1.1302 | 0.6588 | 0.4443 | 0.5626 | 0.5626 | 80.5821 | |
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| 0.0845 | 8.0 | 2192 | 1.1549 | 0.6621 | 0.4488 | 0.5658 | 0.5656 | 80.6204 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.0 |
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- Tokenizers 0.19.1 |
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