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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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- multi_news |
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metrics: |
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- rouge |
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model-index: |
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- name: mt5-small-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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config: default |
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split: validation |
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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: 22.03 |
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- name: Rouge2 |
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type: rouge |
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value: 6.95 |
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- name: Rougel |
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type: rouge |
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value: 18.41 |
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- name: Rougelsum |
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type: rouge |
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value: 18.72 |
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language: |
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- en |
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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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# mt5-small-multi-news |
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This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the multi_news dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.2170 |
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- Rouge1: 22.03 |
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- Rouge2: 6.95 |
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- Rougel: 18.41 |
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- Rougelsum: 18.72 |
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## Intended uses & limitations |
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Text summarization is the inteded use of this model. With further training the model could achieve better results. |
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## Training and evaluation data |
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For the training data we used 10000 samples from the multi-news train dataset. |
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For the evaluation data we used 500 samples from the multi-news evaluation dataset. |
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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: 5.6e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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 | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| |
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| 5.2732 | 1.0 | 1250 | 3.2170 | 22.03 | 6.95 | 18.41 | 18.72 | |
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### Framework versions |
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- Transformers 4.28.0 |
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- Pytorch 2.0.0+cu117 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
