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--- |
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base_model: silmi224/finetune-led-35000 |
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tags: |
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- summarization |
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- generated_from_trainer |
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model-index: |
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- name: led-risalah_data_v15 |
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results: [] |
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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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# led-risalah_data_v15 |
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This model is a fine-tuned version of [silmi224/finetune-led-35000](https://huggingface.co/silmi224/finetune-led-35000) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6673 |
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- Rouge1 Precision: 0.7043 |
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- Rouge1 Recall: 0.1227 |
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- Rouge1 Fmeasure: 0.2067 |
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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: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 4 |
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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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- lr_scheduler_warmup_steps: 300 |
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- num_epochs: 20 |
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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 Precision | Rouge1 Recall | Rouge1 Fmeasure | |
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|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| |
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| 3.0403 | 1.0 | 20 | 2.4986 | 0.5024 | 0.0555 | 0.0987 | |
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| 2.5716 | 2.0 | 40 | 2.1700 | 0.5606 | 0.0817 | 0.1409 | |
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| 2.2879 | 3.0 | 60 | 2.0072 | 0.5705 | 0.0869 | 0.1492 | |
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| 2.0807 | 4.0 | 80 | 1.9094 | 0.6048 | 0.0899 | 0.1542 | |
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| 1.927 | 5.0 | 100 | 1.8184 | 0.5472 | 0.0922 | 0.1561 | |
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| 1.8368 | 6.0 | 120 | 1.7721 | 0.6079 | 0.1036 | 0.1751 | |
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| 1.7468 | 7.0 | 140 | 1.7310 | 0.639 | 0.1095 | 0.1842 | |
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| 1.5913 | 8.0 | 160 | 1.6907 | 0.6637 | 0.1109 | 0.1875 | |
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| 1.534 | 9.0 | 180 | 1.6843 | 0.6355 | 0.1102 | 0.1851 | |
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| 1.4835 | 10.0 | 200 | 1.6605 | 0.6596 | 0.1141 | 0.1922 | |
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| 1.4958 | 11.0 | 220 | 1.6403 | 0.6929 | 0.1162 | 0.1973 | |
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| 1.4547 | 12.0 | 240 | 1.6347 | 0.6781 | 0.1118 | 0.1892 | |
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| 1.3069 | 13.0 | 260 | 1.6604 | 0.6626 | 0.1101 | 0.187 | |
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| 1.2639 | 14.0 | 280 | 1.6712 | 0.697 | 0.1227 | 0.2061 | |
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| 1.3249 | 15.0 | 300 | 1.6255 | 0.6529 | 0.1135 | 0.1914 | |
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| 1.185 | 16.0 | 320 | 1.6484 | 0.6806 | 0.1174 | 0.1981 | |
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| 1.1087 | 17.0 | 340 | 1.6425 | 0.682 | 0.1195 | 0.2008 | |
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| 1.1125 | 18.0 | 360 | 1.6509 | 0.7122 | 0.1235 | 0.2086 | |
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| 1.1574 | 19.0 | 380 | 1.6740 | 0.6983 | 0.1214 | 0.2052 | |
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| 0.9968 | 20.0 | 400 | 1.6673 | 0.7043 | 0.1227 | 0.2067 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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