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--- |
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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: PTS-Bart-Large-CNN |
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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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# PTS-Bart-Large-CNN |
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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: 1.1442 |
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- Rouge1: 0.6591 |
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- Rouge2: 0.449 |
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- Rougel: 0.5635 |
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- Rougelsum: 0.5633 |
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- Gen Len: 78.7977 |
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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 | 220 | 0.8235 | 0.6279 | 0.4019 | 0.5268 | 0.5267 | 82.8295 | |
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| No log | 2.0 | 440 | 0.8053 | 0.6461 | 0.4278 | 0.5486 | 0.5484 | 78.6318 | |
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| 0.7147 | 3.0 | 660 | 0.8889 | 0.6471 | 0.4324 | 0.5491 | 0.5488 | 79.4432 | |
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| 0.7147 | 4.0 | 880 | 0.9679 | 0.6533 | 0.4391 | 0.5538 | 0.5534 | 80.2023 | |
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| 0.2566 | 5.0 | 1100 | 0.9734 | 0.6563 | 0.4422 | 0.5574 | 0.5571 | 78.9727 | |
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| 0.2566 | 6.0 | 1320 | 1.0504 | 0.6538 | 0.4436 | 0.559 | 0.5585 | 78.5682 | |
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| 0.1136 | 7.0 | 1540 | 1.1172 | 0.6591 | 0.4474 | 0.5646 | 0.5647 | 78.6068 | |
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| 0.1136 | 8.0 | 1760 | 1.1442 | 0.6591 | 0.449 | 0.5635 | 0.5633 | 78.7977 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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