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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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- generated_from_trainer
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datasets:
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- kp20k
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metrics:
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- rouge
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model-index:
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- name: ED_keyphrase_roberta/
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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: kp20k
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type: kp20k
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config: generation
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split: train[:15%]
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args: generation
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metrics:
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- name: Rouge1
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type: rouge
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value: 0.1132
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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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# ED_keyphrase_roberta/
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This model is a fine-tuned version of [](https://huggingface.co/) on the kp20k dataset.
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It achieves the following results on the evaluation set:
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- Loss: 4.6070
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- Rouge1: 0.1132
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- Rouge2: 0.0161
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- Rougel: 0.108
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- Rougelsum: 0.1081
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- Gen Len: 10.9056
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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: 32
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- eval_batch_size: 32
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 64
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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: 4
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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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| 6.2014 | 1.0 | 664 | 5.4438 | 0.0532 | 0.0021 | 0.0525 | 0.0524 | 9.0955 |
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| 5.3993 | 2.0 | 1328 | 4.8016 | 0.0958 | 0.0105 | 0.0921 | 0.0921 | 11.524 |
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| 4.9398 | 3.0 | 1992 | 4.6499 | 0.1095 | 0.0153 | 0.1049 | 0.1048 | 11.2748 |
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| 4.6497 | 4.0 | 2656 | 4.6070 | 0.1132 | 0.0161 | 0.108 | 0.1081 | 10.9056 |
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### Framework versions
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- Transformers 4.25.1
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- Pytorch 1.13.0+cu116
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- Datasets 2.8.0
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- Tokenizers 0.13.2
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