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README.md
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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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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: finetuned_multi_news_bart_text_summarisation
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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: test
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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: 0.4038
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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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# finetuned_multi_news_bart_text_summarisation
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This model is a fine-tuned version of [slauw87/bart_summarisation](https://huggingface.co/slauw87/bart_summarisation) on the multi_news dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.8952
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- Rouge1: 0.4038
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- Rouge2: 0.1389
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- Rougel: 0.2155
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- Rougelsum: 0.2147
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- Gen Len: 138.7667
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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: 2
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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 | 15 | 2.9651 | 0.3903 | 0.134 | 0.21 | 0.2098 | 137.6 |
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| No log | 2.0 | 30 | 2.8952 | 0.4038 | 0.1389 | 0.2155 | 0.2147 | 138.7667 |
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### Framework versions
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- Transformers 4.30.2
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- Pytorch 2.0.1+cu118
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- Datasets 2.13.1
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- Tokenizers 0.13.3
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