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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-cnn-finetuned-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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args: default |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 42.0423 |
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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-large-cnn-finetuned-multi-news |
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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: 2.0950 |
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- Rouge1: 42.0423 |
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- Rouge2: 14.8812 |
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- Rougel: 23.3412 |
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- Rougelsum: 36.2613 |
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## Model description |
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bart-large-cnn fine tuned on sample of multi-news dataset |
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## Intended uses & limitations |
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The intended use of the model is for downstream summarization tasks but it's limited to input text 1024 words. Any text longer than that would be truncated. |
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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: 2e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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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: 1 |
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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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| 2.2037 | 1.0 | 750 | 2.0950 | 42.0423 | 14.8812 | 23.3412 | 36.2613 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 2.0.0 |
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- Tokenizers 0.11.6 |
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