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bert-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-multi_news
keith97/bert-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-multi_news
bert-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-multi_news is a machine learning model from keith97. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- encoder-decoder
- Access
- Public
- Created
- Nov 6, 2022
- Updated
- Nov 6, 2022
- SHA
- 4d72e76a
Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
bert-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-multi_news
This model is a fine-tuned version of mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization on the multi_news dataset. It achieves the following results on the evaluation set:
- Loss: 4.3760
- Rouge1: 38.5318
- Rouge2: 12.7285
- Rougel: 21.4358
- Rougelsum: 33.4565
- Gen Len: 128.985
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 4.6946 | 0.89 | 400 | 4.5393 | 37.164 | 11.5191 | 20.2519 | 32.1568 | 126.415 |
| 4.5128 | 1.78 | 800 | 4.4185 | 38.2345 | 12.2053 | 20.954 | 33.0667 | 128.975 |
| 4.2926 | 2.67 | 1200 | 4.3866 | 38.4475 | 12.6488 | 21.3046 | 33.2768 | 129.0 |
| 4.231 | 3.56 | 1600 | 4.3808 | 38.7008 | 12.6323 | 21.307 | 33.3693 | 128.955 |
| 4.125 | 4.44 | 2000 | 4.3760 | 38.5318 | 12.7285 | 21.4358 | 33.4565 | 128.985 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
- Library
- transformers
- Type
- encoder-decoder
- License
- apache-2.0
- Created
- Nov 6, 2022
- Updated
- Nov 6, 2022