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--- |
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license: apache-2.0 |
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base_model: ainize/bart-base-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-base-cnn-YT-transcript-sum |
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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-cnn-YT-transcript-sum |
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This model is a fine-tuned version of [ainize/bart-base-cnn](https://huggingface.co./ainize/bart-base-cnn) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4969 |
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- Rouge1: 27.1516 |
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- Rouge2: 14.6227 |
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- Rougel: 23.3968 |
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- Rougelsum: 25.4786 |
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- Gen Len: 19.9954 |
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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: 5e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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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: 15 |
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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 | 216 | 1.5374 | 24.7307 | 11.5124 | 20.6823 | 22.9189 | 19.9630 | |
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| No log | 2.0 | 432 | 1.4976 | 26.825 | 14.0512 | 23.2078 | 25.2044 | 19.9583 | |
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| 1.5449 | 3.0 | 648 | 1.4969 | 27.1516 | 14.6227 | 23.3968 | 25.4786 | 19.9954 | |
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| 1.5449 | 4.0 | 864 | 1.5345 | 27.2526 | 15.0873 | 23.8556 | 25.7798 | 19.9861 | |
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| 0.9 | 5.0 | 1080 | 1.5962 | 26.8267 | 14.7267 | 23.2263 | 25.2149 | 19.9676 | |
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| 0.9 | 6.0 | 1296 | 1.6378 | 26.8444 | 14.8753 | 23.254 | 25.2943 | 19.9815 | |
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| 0.5749 | 7.0 | 1512 | 1.6819 | 27.1776 | 14.898 | 23.2454 | 25.4298 | 19.9583 | |
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| 0.5749 | 8.0 | 1728 | 1.7360 | 26.9518 | 15.308 | 23.6574 | 25.2991 | 19.9769 | |
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| 0.5749 | 9.0 | 1944 | 1.7796 | 27.9253 | 15.7998 | 24.4827 | 26.4424 | 19.9769 | |
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| 0.3668 | 10.0 | 2160 | 1.8078 | 26.9211 | 15.0903 | 23.4484 | 25.4369 | 19.9815 | |
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| 0.3668 | 11.0 | 2376 | 1.8405 | 27.4434 | 15.3608 | 23.903 | 25.8117 | 19.9861 | |
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| 0.255 | 12.0 | 2592 | 1.8447 | 27.7175 | 15.7173 | 24.2096 | 26.0946 | 19.9815 | |
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| 0.255 | 13.0 | 2808 | 1.8834 | 27.2409 | 15.3865 | 23.7314 | 25.7682 | 19.9815 | |
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| 0.192 | 14.0 | 3024 | 1.8796 | 27.2939 | 15.5502 | 23.8294 | 25.7409 | 19.9815 | |
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| 0.192 | 15.0 | 3240 | 1.8851 | 27.6741 | 15.771 | 24.1976 | 26.1196 | 19.9722 | |
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### Framework versions |
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- Transformers 4.33.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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