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--- |
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license: apache-2.0 |
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language: |
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- en |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: t5-small-finetuned-turk-text-simplification |
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results: [] |
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widget: |
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- text: "simplify: the incident has been the subject of numerous reports as to ethics in scholarship ." |
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- text: "simplify: the historical method comprises the techniques and guidelines by which historians use primary sources and other evidence to research and then to write history ." |
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- text: "simplify: none of the authors , contributors , sponsors , administrators , vandals , or anyone else connected with wikipedia , in any way whatsoever , can be responsible for your use of the information contained in or linked from these web pages ." |
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- text: "simplify: oregano is an indispensable ingredient in greek cuisine ." |
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inference: |
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parameters: |
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temperature: 1.5 |
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max_length: 256 |
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do_sample: True |
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num_beams: 3 |
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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) finetuned-turk-text-simplification |
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1001 |
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- Rouge2 Precision: 0.6825 |
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- Rouge2 Recall: 0.4542 |
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- Rouge2 Fmeasure: 0.5221 |
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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: 5e-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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- 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 | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |
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|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| |
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| 0.4318 | 1.0 | 500 | 0.1053 | 0.682 | 0.4533 | 0.5214 | |
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| 0.0977 | 2.0 | 1000 | 0.1019 | 0.683 | 0.4545 | 0.5225 | |
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| 0.0938 | 3.0 | 1500 | 0.1010 | 0.6828 | 0.4547 | 0.5226 | |
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| 0.0916 | 4.0 | 2000 | 0.1003 | 0.6829 | 0.4545 | 0.5225 | |
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| 0.0906 | 5.0 | 2500 | 0.1001 | 0.6825 | 0.4542 | 0.5221 | |
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### Framework versions |
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- Transformers 4.21.3 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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