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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- scientific_papers
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metrics:
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- rouge
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model-index:
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- name: t5-small-science-papers
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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: scientific_papers
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type: scientific_papers
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config: arxiv
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split: train
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args: arxiv
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metrics:
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- name: Rouge1
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type: rouge
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value: 12.3568
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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-small-science-papers
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the scientific_papers dataset.
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It achieves the following results on the evaluation set:
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- Loss: 3.6405
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- Rouge1: 12.3568
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- Rouge2: 2.4449
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- Rougel: 10.2371
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- Rougelsum: 11.4209
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- Gen Len: 19.0
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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: 16
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- eval_batch_size: 16
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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: 5
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- mixed_precision_training: Native AMP
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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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| 4.4735 | 1.0 | 12690 | 4.3727 | 9.9604 | 1.7641 | 8.6213 | 9.2779 | 19.0 |
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| 4.0104 | 2.0 | 25380 | 3.9384 | 11.4001 | 2.1474 | 9.6516 | 10.6602 | 19.0 |
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| 3.8237 | 3.0 | 38070 | 3.7580 | 11.1806 | 2.1229 | 9.3881 | 10.3853 | 19.0 |
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| 3.7382 | 4.0 | 50760 | 3.6738 | 11.9298 | 2.3222 | 9.9077 | 11.045 | 19.0 |
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| 3.6994 | 5.0 | 63450 | 3.6405 | 12.3568 | 2.4449 | 10.2371 | 11.4209 | 19.0 |
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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 2.6.1
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- Tokenizers 0.13.1
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