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update model card README.md
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README.md
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---
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tags:
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- paraphrasing
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- generated_from_trainer
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datasets:
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- paws
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metrics:
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- rouge
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model-index:
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- name: pegasus-pubmed-finetuned-paws
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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: paws
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type: paws
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args: labeled_final
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metrics:
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- name: Rouge1
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type: rouge
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value: 56.8108
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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-pubmed-finetuned-paws
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This model is a fine-tuned version of [google/pegasus-pubmed](https://huggingface.co/google/pegasus-pubmed) on the paws dataset.
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It achieves the following results on the evaluation set:
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- Loss: 3.5012
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- Rouge1: 56.8108
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- Rouge2: 36.2576
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- Rougel: 51.1666
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- Rougelsum: 51.2193
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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: 0.0001
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- train_batch_size: 16
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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: 5
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- mixed_precision_training: Native AMP
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- label_smoothing_factor: 0.1
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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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| No log | 0.73 | 1000 | 3.8839 | 51.2731 | 29.8072 | 45.767 | 45.5732 |
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| 4.071 | 1.47 | 2000 | 3.6459 | 52.756 | 31.9185 | 48.0092 | 48.0544 |
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| 3.5467 | 2.2 | 3000 | 3.5849 | 54.8127 | 33.1959 | 49.326 | 49.4971 |
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| 3.5467 | 2.93 | 4000 | 3.5267 | 55.387 | 33.9516 | 50.683 | 50.6313 |
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| 3.3654 | 3.66 | 5000 | 3.5031 | 57.5279 | 35.2664 | 51.9903 | 52.258 |
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| 3.2844 | 4.4 | 6000 | 3.5296 | 56.0536 | 33.395 | 50.9909 | 51.244 |
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### Framework versions
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- Transformers 4.18.0
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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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