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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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- mlsum |
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metrics: |
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- rouge |
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model-index: |
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- name: eval-bart-turkish |
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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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--- |
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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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# mukayese/bart-turkish-mlsum |
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This model is a 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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## 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: 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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### 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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| 4.4304 | 1.0 | 3895 | 4.3749 | 33.2844 | 22.8262 | 29.9423 | 30.7953 | 19.7732 | |
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| 3.65 | 2.0 | 7790 | 3.7414 | 33.8392 | 23.517 | 30.4871 | 31.3309 | 19.9031 | |
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| 3.397 | 3.0 | 11685 | 3.5651 | 34.2335 | 23.9113 | 30.9237 | 31.7434 | 19.894 | |
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| 3.2202 | 4.0 | 15580 | 3.5054 | 34.2535 | 23.9595 | 30.9811 | 31.7961 | 19.9212 | |
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| 3.0827 | 5.0 | 19475 | 3.4547 | 34.5545 | 24.1991 | 31.2609 | 32.085 | 19.9195 | |
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| 2.9801 | 6.0 | 23370 | 3.4328 | 34.6721 | 24.2537 | 31.372 | 32.1777 | 19.9331 | |
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| 2.8689 | 7.0 | 27265 | 3.4377 | 34.6764 | 24.3314 | 31.4376 | 32.1981 | 19.9278 | |
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| 2.7813 | 8.0 | 31160 | 3.4407 | 34.746 | 24.345 | 31.4511 | 32.2708 | 19.9468 | |
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| 2.6848 | 9.0 | 35055 | 3.4539 | 34.7376 | 24.3224 | 31.4784 | 32.2817 | 19.9096 | |
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| 2.5974 | 10.0 | 38950 | 3.4683 | 34.9174 | 24.4716 | 31.5641 | 32.4039 | 19.9384 | |
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| 2.5228 | 11.0 | 42845 | 3.4903 | 34.9845 | 24.4972 | 31.6585 | 32.4753 | 19.93 | |
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| 2.4633 | 12.0 | 46740 | 3.5105 | 34.8496 | 24.3559 | 31.5256 | 32.3635 | 19.9275 | |
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| 2.4022 | 13.0 | 50635 | 3.5234 | 34.9109 | 24.4008 | 31.5449 | 32.4021 | 19.9374 | |
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| 2.3605 | 14.0 | 54530 | 3.5306 | 34.9545 | 24.4365 | 31.6208 | 32.4711 | 19.9366 | |
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| 2.3216 | 15.0 | 58425 | 3.5379 | 34.9079 | 24.4077 | 31.5734 | 32.4287 | 19.9365 | |
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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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