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update model card README.md
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README.md
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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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