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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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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-newsroom-filtered |
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results: [] |
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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-newsroom-filtered |
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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 an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.5413 |
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- Rouge1: 32.3232 |
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- Rouge2: 20.9203 |
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- Rougel: 27.232 |
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- Rougelsum: 29.345 |
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- Gen Len: 72.2217 |
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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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| 3.796 | 0.89 | 405 | 3.6945 | 29.7168 | 17.6705 | 24.4204 | 26.484 | 69.6847 | |
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| 3.6426 | 1.78 | 810 | 3.5532 | 32.3051 | 20.8789 | 27.1724 | 29.384 | 72.3695 | |
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| 3.2645 | 2.66 | 1215 | 3.5437 | 32.2016 | 20.758 | 27.083 | 29.0954 | 73.3892 | |
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| 3.1719 | 3.55 | 1620 | 3.5377 | 32.5493 | 21.083 | 27.0881 | 29.4691 | 71.5222 | |
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| 2.9763 | 4.44 | 2025 | 3.5413 | 32.3232 | 20.9203 | 27.232 | 29.345 | 72.2217 | |
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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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