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Quantization made by Richard Erkhov. |
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[Github](https://github.com/RichardErkhov) |
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[Discord](https://discord.gg/pvy7H8DZMG) |
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[Request more models](https://github.com/RichardErkhov/quant_request) |
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transformer-turkish-summarization - bnb 4bits |
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- Model creator: https://huggingface.co/mukayese/ |
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- Original model: https://huggingface.co/mukayese/transformer-turkish-summarization/ |
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Original model description: |
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--- |
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datasets: |
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- mlsum |
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metrics: |
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- rouge |
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model-index: |
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- name: mukayese/transformer-turkish-summarization |
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results: |
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- task: |
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name: Summarization |
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type: summarization |
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dataset: |
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name: mlsum tu |
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type: mlsum |
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args: tu |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 43.2049 |
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license: mit |
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language: |
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- tr |
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pipeline_tag: summarization |
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--- |
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# [Mukayese: Turkish NLP Strikes Back](https://arxiv.org/abs/2203.01215) |
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## Summarization: mukayese/transformer-turkish-summarization |
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_This model is uncased_, it was initialized from scratch and trained only the mlsum/tu dataset with no pre-training. |
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It achieves the following results on the evaluation set: |
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- Rouge1: 43.2049 |
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- Rouge2: 30.7082 |
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- Rougel: 38.1981 |
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- Rougelsum: 39.9453 |
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Check [this](https://arxiv.org/abs/2203.01215) paper for more details on the model and the dataset. |
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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: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 64 |
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- total_eval_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: 15.0 |
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- mixed_precision_training: Native AMP |
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- label_smoothing_factor: 0.1 |
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### Framework versions |
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- Transformers 4.11.3 |
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- Pytorch 1.8.2+cu111 |
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- Datasets 1.14.0 |
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- Tokenizers 0.10.3 |
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### Citation |
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``` |
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@misc{safaya-etal-2022-mukayese, |
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title={Mukayese: Turkish NLP Strikes Back}, |
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author={Ali Safaya and Emirhan Kurtuluş and Arda Göktoğan and Deniz Yuret}, |
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year={2022}, |
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eprint={2203.01215}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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