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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: bart-base-multi-news |
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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: train |
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split: validation |
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args: train |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 27.57 |
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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-multi-news |
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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: 2.9167 |
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- Rouge1: 27.57 |
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- Rouge2: 8.53 |
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- Rougel: 15.17 |
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- Rougelsum: 18.03 |
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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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| 2.8539 | 1.0 | 1250 | 2.5026 | 27.57 | 8.53 | 15.17 | 18.03 | |
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| 2.3547 | 2.0 | 2500 | 2.5102 | 27.57 | 8.53 | 15.17 | 18.03 | |
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| 2.0079 | 3.0 | 3750 | 2.5593 | 27.57 | 8.53 | 15.17 | 18.03 | |
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| 1.7303 | 4.0 | 5000 | 2.6260 | 27.57 | 8.53 | 15.17 | 18.03 | |
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| 1.4993 | 5.0 | 6250 | 2.7184 | 27.57 | 8.53 | 15.17 | 18.03 | |
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| 1.3136 | 6.0 | 7500 | 2.8246 | 27.57 | 8.53 | 15.17 | 18.03 | |
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| 1.1718 | 7.0 | 8750 | 2.8684 | 27.57 | 8.53 | 15.17 | 18.03 | |
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| 1.0729 | 8.0 | 10000 | 2.9167 | 27.57 | 8.53 | 15.17 | 18.03 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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