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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-CNN-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-CNN-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: 2.2222 |
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- Rouge1: 0.4398 |
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- Rouge2: 0.1996 |
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- Rougel: 0.2964 |
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- Rougelsum: 0.4096 |
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- Gen Len: 95.364 |
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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: 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: 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 | 250 | 1.4136 | 0.4361 | 0.2058 | 0.2957 | 0.4075 | 99.678 | |
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| 1.3139 | 2.0 | 500 | 1.4521 | 0.444 | 0.2085 | 0.3035 | 0.4138 | 90.808 | |
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| 1.3139 | 3.0 | 750 | 1.5573 | 0.4409 | 0.2046 | 0.2945 | 0.4102 | 100.502 | |
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| 0.7471 | 4.0 | 1000 | 1.6873 | 0.4429 | 0.205 | 0.2985 | 0.4119 | 96.34 | |
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| 0.7471 | 5.0 | 1250 | 1.8544 | 0.4395 | 0.2016 | 0.2964 | 0.409 | 100.1 | |
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| 0.4392 | 6.0 | 1500 | 2.0239 | 0.4407 | 0.2012 | 0.2946 | 0.4085 | 97.476 | |
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| 0.4392 | 7.0 | 1750 | 2.1492 | 0.4409 | 0.199 | 0.2947 | 0.4101 | 94.41 | |
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| 0.2886 | 8.0 | 2000 | 2.2222 | 0.4398 | 0.1996 | 0.2964 | 0.4096 | 95.364 | |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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