| | ---
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| | license: apache-2.0
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| | base_model: psyche/KoT5-summarization
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| | tags:
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| | - generated_from_trainer
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| | model-index:
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| | - name: KoT5-summarization-mydata
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| | results: []
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| | ---
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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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| |
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| | # KoT5-summarization-mydata
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| |
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| | This model is a fine-tuned version of [psyche/KoT5-summarization](https://huggingface.co/psyche/KoT5-summarization) on the None dataset.
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| | It achieves the following results on the evaluation set:
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| | - Loss: 0.7827
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| | - Rouge1 Precision: 0.5055
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| | - Rouge1 Recall: 0.5287
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| | - Rouge1 F1: 0.5102
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| | - Rouge2 Precision: 0.3635
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| | - Rouge2 Recall: 0.3790
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| | - Rouge2 F1: 0.3660
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| | - Rouge3 Precision: 0.2739
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| | - Rouge3 Recall: 0.2852
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| | - Rouge3 F1: 0.2753
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| |
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| | ## Model description
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| |
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| | More information needed
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| |
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| | ## Intended uses & limitations
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| |
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| | More information needed
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| |
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| | ## Training and evaluation data
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| |
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| | More information needed
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| |
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| | ## Training procedure
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| |
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| | ### Training hyperparameters
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| |
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| | The following hyperparameters were used during training:
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| | - learning_rate: 8e-06
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| | - train_batch_size: 2
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| | - eval_batch_size: 2
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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: 2
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| |
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| | ### Training results
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| |
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| | | Training Loss | Epoch | Step | Validation Loss | Rouge1 Precision | Rouge1 Recall | Rouge1 F1 | Rouge2 Precision | Rouge2 Recall | Rouge2 F1 | Rouge3 Precision | Rouge3 Recall | Rouge3 F1 |
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| | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------:|:----------------:|:-------------:|:---------:|:----------------:|:-------------:|:---------:|
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| | | 0.8855 | 1.0 | 34033 | 0.7909 | 0.5110 | 0.5294 | 0.5134 | 0.3681 | 0.3805 | 0.3691 | 0.2803 | 0.2892 | 0.2805 |
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| | | 0.8206 | 2.0 | 68066 | 0.7827 | 0.5055 | 0.5287 | 0.5102 | 0.3635 | 0.3790 | 0.3660 | 0.2739 | 0.2852 | 0.2753 |
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| |
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| | ### Framework versions
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| |
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| | - Transformers 4.40.2
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| | - Pytorch 2.8.0+cu128
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| | - Datasets 2.19.0
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| | - Tokenizers 0.19.1
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| |
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