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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: Abmiguity-factor |
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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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# Abmiguity-factor |
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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: 0.8537 |
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- Rouge1: 0.5239 |
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- Rouge2: 0.2727 |
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- Rougel: 0.3876 |
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- Rougelsum: 0.3876 |
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- Gen Len: 90.5 |
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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 | 1 | 1.2998 | 0.3903 | 0.1429 | 0.2699 | 0.2699 | 69.0 | |
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| No log | 2.0 | 2 | 1.1258 | 0.4737 | 0.202 | 0.3449 | 0.3449 | 77.0 | |
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| No log | 3.0 | 3 | 1.0220 | 0.4627 | 0.2003 | 0.3372 | 0.3372 | 87.5 | |
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| No log | 4.0 | 4 | 0.9522 | 0.472 | 0.2042 | 0.3429 | 0.3429 | 85.5 | |
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| No log | 5.0 | 5 | 0.9162 | 0.4951 | 0.2238 | 0.3814 | 0.3814 | 95.0 | |
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| No log | 6.0 | 6 | 0.8882 | 0.4951 | 0.2238 | 0.3814 | 0.3814 | 95.0 | |
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| No log | 7.0 | 7 | 0.8659 | 0.5171 | 0.2652 | 0.4122 | 0.4122 | 97.5 | |
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| No log | 8.0 | 8 | 0.8537 | 0.5239 | 0.2727 | 0.3876 | 0.3876 | 90.5 | |
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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 3.0.1 |
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- Tokenizers 0.19.1 |
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