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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: bert-small2bert-small-finetuned-cnn_daily_mail-summarization-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: 38.5318 |
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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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# bert-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-multi_news |
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This model is a fine-tuned version of [mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization](https://huggingface.co/mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization) on the multi_news dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 4.3760 |
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- Rouge1: 38.5318 |
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- Rouge2: 12.7285 |
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- Rougel: 21.4358 |
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- Rougelsum: 33.4565 |
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- Gen Len: 128.985 |
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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: 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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- 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: 5 |
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- mixed_precision_training: Native AMP |
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- label_smoothing_factor: 0.1 |
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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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| 4.6946 | 0.89 | 400 | 4.5393 | 37.164 | 11.5191 | 20.2519 | 32.1568 | 126.415 | |
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| 4.5128 | 1.78 | 800 | 4.4185 | 38.2345 | 12.2053 | 20.954 | 33.0667 | 128.975 | |
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| 4.2926 | 2.67 | 1200 | 4.3866 | 38.4475 | 12.6488 | 21.3046 | 33.2768 | 129.0 | |
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| 4.231 | 3.56 | 1600 | 4.3808 | 38.7008 | 12.6323 | 21.307 | 33.3693 | 128.955 | |
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| 4.125 | 4.44 | 2000 | 4.3760 | 38.5318 | 12.7285 | 21.4358 | 33.4565 | 128.985 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.11.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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