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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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- summarization |
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- generated_from_trainer |
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datasets: |
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- tldr_news |
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metrics: |
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- rouge |
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model-index: |
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- name: my_summ |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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dataset: |
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name: tldr_news |
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type: tldr_news |
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config: all |
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split: test |
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args: all |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.21647643221587914 |
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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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# my_summ |
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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 tldr_news dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 4.1133 |
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- Rouge1: 0.2165 |
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- Rouge2: 0.0872 |
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- Rougel: 0.1846 |
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- Rougelsum: 0.1881 |
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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: 5.6e-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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| |
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| 2.2607 | 1.0 | 125 | 2.2706 | 0.2318 | 0.0950 | 0.1983 | 0.2024 | |
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| 1.1698 | 2.0 | 250 | 2.3624 | 0.2150 | 0.0848 | 0.1828 | 0.1856 | |
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| 0.5798 | 3.0 | 375 | 2.8369 | 0.2144 | 0.0838 | 0.1802 | 0.1848 | |
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| 0.2813 | 4.0 | 500 | 3.3045 | 0.2112 | 0.0803 | 0.1788 | 0.1821 | |
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| 0.1544 | 5.0 | 625 | 3.6092 | 0.2096 | 0.0793 | 0.1780 | 0.1838 | |
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| 0.0862 | 6.0 | 750 | 3.7615 | 0.2168 | 0.0848 | 0.1851 | 0.1881 | |
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| 0.0518 | 7.0 | 875 | 3.9039 | 0.2180 | 0.0861 | 0.1842 | 0.1873 | |
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| 0.0253 | 8.0 | 1000 | 4.1133 | 0.2165 | 0.0872 | 0.1846 | 0.1881 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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