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--- |
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license: mit |
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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: bart_large_summarise_v3 |
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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 |
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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: 0.3914 |
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--- |
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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 multi_news dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 4.1359 |
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- Rouge1: 0.3914 |
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- Rouge2: 0.1399 |
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- Rougel: 0.2039 |
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- Rougelsum: 0.3504 |
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- Gen Len: 141.64 |
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## Model description |
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This model was created to generate summaries of news articles. |
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## Intended uses & limitations |
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The model takes up to maximum article length of 1024 tokens and generates a summary of maximum length of 512 tokens. |
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## Training and evaluation data |
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This model was trained on 1000 articles and summaries from the Multi-News dataset. https://arxiv.org/abs/1906.01749 |
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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: 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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 10 |
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- label_smoothing_factor: 0.1 |
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### Framework versions |
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- Transformers 4.23.1 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.1 |
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