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--- |
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license: apache-2.0 |
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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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- multi_news |
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metrics: |
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- rouge |
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model-index: |
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- name: multi-news-diff-weight |
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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: multi_news |
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type: multi_news |
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config: default |
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split: train[:20%] |
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args: default |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 9.9082 |
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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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# multi-news-diff-weight |
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This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the multi_news dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.5350 |
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- Rouge1: 9.9082 |
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- Rouge2: 3.6995 |
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- Rougel: 7.6135 |
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- Rougelsum: 9.0176 |
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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: 2 |
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- eval_batch_size: 2 |
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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: 5 |
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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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| 2.8555 | 1.0 | 4047 | 2.5846 | 9.7797 | 3.6212 | 7.5597 | 8.9387 | |
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| 2.5262 | 2.0 | 8094 | 2.5231 | 9.7969 | 3.5968 | 7.5592 | 8.9532 | |
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| 2.3195 | 3.0 | 12141 | 2.5149 | 9.83 | 3.6338 | 7.5109 | 8.9725 | |
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| 2.1655 | 4.0 | 16188 | 2.5188 | 9.8704 | 3.6936 | 7.6094 | 9.0336 | |
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| 2.055 | 5.0 | 20235 | 2.5350 | 9.9082 | 3.6995 | 7.6135 | 9.0176 | |
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### Framework versions |
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- Transformers 4.29.1 |
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- Pytorch 2.0.0 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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