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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- reddit |
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metrics: |
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- rouge |
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model-index: |
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- name: pegasus-xsum-reddit-clean-4 |
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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: reddit |
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type: reddit |
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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: 27.7525 |
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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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# pegasus-xsum-reddit-clean-4 |
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This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on the reddit dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.7697 |
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- Rouge1: 27.7525 |
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- Rouge2: 7.9823 |
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- Rougel: 20.9276 |
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- Rougelsum: 22.6678 |
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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: 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: 3 |
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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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| 3.0594 | 1.0 | 1906 | 2.8489 | 27.9837 | 8.0824 | 20.9135 | 22.7261 | |
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| 2.861 | 2.0 | 3812 | 2.7793 | 27.8298 | 8.048 | 20.8653 | 22.6781 | |
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| 2.7358 | 3.0 | 5718 | 2.7697 | 27.7525 | 7.9823 | 20.9276 | 22.6678 | |
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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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