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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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