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--- |
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license: apache-2.0 |
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tags: |
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- summarization |
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- generated_from_trainer |
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datasets: |
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- cnn_dailymail |
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model-index: |
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- name: bart-base-finetuned-summarization-cnn-ver3 |
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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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# bart-base-finetuned-summarization-cnn-ver3 |
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This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the cnn_dailymail dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.9827 |
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- Bertscore-mean-precision: 0.8811 |
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- Bertscore-mean-recall: 0.8554 |
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- Bertscore-mean-f1: 0.8679 |
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- Bertscore-median-precision: 0.8809 |
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- Bertscore-median-recall: 0.8545 |
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- Bertscore-median-f1: 0.8669 |
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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: 0.0003 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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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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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bertscore-mean-precision | Bertscore-mean-recall | Bertscore-mean-f1 | Bertscore-median-precision | Bertscore-median-recall | Bertscore-median-f1 | |
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|:-------------:|:-----:|:----:|:---------------:|:------------------------:|:---------------------:|:-----------------:|:--------------------------:|:-----------------------:|:-------------------:| |
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| 3.632 | 1.0 | 5742 | 2.9827 | 0.8811 | 0.8554 | 0.8679 | 0.8809 | 0.8545 | 0.8669 | |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.2 |
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