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--- |
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license: bsd-3-clause |
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tags: |
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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: long-t5-tglobal-base-mediasum |
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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: train[:20000] |
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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: 0.3246 |
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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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# long-t5-tglobal-base-mediasum |
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This model is a fine-tuned version of [pszemraj/long-t5-tglobal-base-16384-book-summary](https://huggingface.co/pszemraj/long-t5-tglobal-base-16384-book-summary) on the multi_news dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.0387 |
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- Rouge1: 0.3246 |
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- Rouge2: 0.0867 |
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- Rougel: 0.1663 |
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- Rougelsum: 0.1662 |
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- Gen Len: 106.985 |
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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: 2e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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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 | Gen Len | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
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| 2.4191 | 1.0 | 4500 | 2.0952 | 0.3389 | 0.0882 | 0.1706 | 0.1706 | 118.285 | |
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| 2.3462 | 2.0 | 9000 | 2.0484 | 0.3339 | 0.0887 | 0.1683 | 0.1683 | 111.936 | |
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| 2.3458 | 3.0 | 13500 | 2.0387 | 0.3246 | 0.0867 | 0.1663 | 0.1662 | 106.985 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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