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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: t5-base-finetuned-summarization-cnn-ver2 |
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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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# t5-base-finetuned-summarization-cnn-ver2 |
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This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the cnn_dailymail dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.7601 |
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- Bertscore-mean-precision: 0.8926 |
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- Bertscore-mean-recall: 0.8628 |
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- Bertscore-mean-f1: 0.8772 |
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- Bertscore-median-precision: 0.8906 |
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- Bertscore-median-recall: 0.8600 |
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- Bertscore-median-f1: 0.8751 |
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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: 5e-05 |
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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: 3 |
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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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| 1.4581 | 1.0 | 5742 | 1.6800 | 0.8904 | 0.8615 | 0.8755 | 0.8887 | 0.8597 | 0.8737 | |
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| 1.2356 | 2.0 | 11484 | 1.7274 | 0.8924 | 0.8626 | 0.8771 | 0.8911 | 0.8607 | 0.8753 | |
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| 1.1073 | 3.0 | 17226 | 1.7601 | 0.8926 | 0.8628 | 0.8772 | 0.8906 | 0.8600 | 0.8751 | |
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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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