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---
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license: apache-2.0
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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-base-finetuned-summscreen-bestval-100-genlen-10-epochs
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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-base-finetuned-summscreen-bestval-100-genlen-10-epochs
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This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 3.0979
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- Rouge1: 31.5373
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- Rouge2: 6.6821
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- Rougel: 18.6754
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- Rougelsum: 27.4448
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- Gen Len: 80.1927
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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: 1e-05
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- train_batch_size: 1
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- eval_batch_size: 1
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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: 10
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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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| 3.4849 | 0.99 | 3500 | 3.2071 | 28.6828 | 5.2634 | 17.218 | 25.487 | 94.059 |
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| 3.2933 | 1.99 | 7000 | 3.1329 | 29.9774 | 5.7038 | 17.7705 | 26.2492 | 88.2358 |
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| 3.1088 | 2.98 | 10500 | 3.1010 | 29.6903 | 5.6976 | 17.7468 | 25.9472 | 81.3129 |
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| 2.9605 | 3.98 | 14000 | 3.0811 | 30.2088 | 6.1092 | 18.157 | 26.3051 | 77.8844 |
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| 2.8778 | 4.97 | 17500 | 3.0747 | 30.6996 | 6.3038 | 18.4725 | 26.8669 | 81.6168 |
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| 2.788 | 5.97 | 21000 | 3.0896 | 30.7478 | 6.4468 | 18.3755 | 26.8789 | 85.6395 |
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| 2.7218 | 6.96 | 24500 | 3.0961 | 30.994 | 6.4407 | 18.4929 | 26.9802 | 79.1315 |
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| 2.6753 | 7.96 | 28000 | 3.0892 | 31.336 | 6.6768 | 18.8122 | 27.389 | 83.2313 |
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| 2.5753 | 8.95 | 31500 | 3.0960 | 31.3248 | 6.4093 | 18.6552 | 27.2087 | 80.1474 |
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| 2.5918 | 9.95 | 35000 | 3.0979 | 31.5373 | 6.6821 | 18.6754 | 27.4448 | 80.1927 |
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### Framework versions
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- Transformers 4.26.0
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- Pytorch 1.13.1
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- Datasets 2.9.0
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- Tokenizers 0.13.2
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