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--- |
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license: apache-2.0 |
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base_model: google/flan-t5-large |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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- f1 |
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- recall |
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- precision |
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model-index: |
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- name: KGAQ-2 |
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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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# KGAQ-2 |
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This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.6712 |
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- Rouge1: 9.9002 |
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- Rouge2: 0.817 |
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- Rougel: 9.31 |
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- Rougelsum: 9.8757 |
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- Gen Len: 4.0 |
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- F1: 0.0005 |
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- Recall: 0.0008 |
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- Precision: 0.0003 |
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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: 0.001 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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: cosine_with_restarts |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | F1 | Recall | Precision | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|:------:|:------:|:---------:| |
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| 3.5701 | 1.0 | 598 | 3.3914 | 14.1052 | 1.2078 | 13.0257 | 14.1332 | 3.0 | 0.0 | 0.0 | 0.0 | |
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| 3.0379 | 2.0 | 1196 | 2.7468 | 12.4379 | 1.0435 | 11.3645 | 12.4814 | 3.0 | 0.0005 | 0.0008 | 0.0003 | |
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| 2.2773 | 3.0 | 1794 | 2.4962 | 25.6591 | 2.6653 | 16.5422 | 25.687 | 6.0 | 0.0 | 0.0 | 0.0 | |
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| 1.8845 | 4.0 | 2392 | 2.4370 | 8.8131 | 0.2887 | 8.1866 | 8.8014 | 3.0 | 0.0005 | 0.0008 | 0.0003 | |
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| 1.7721 | 5.0 | 2990 | 2.5342 | 8.2864 | 0.5105 | 7.6569 | 8.2655 | 3.0 | 0.0005 | 0.0008 | 0.0003 | |
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| 2.1007 | 6.0 | 3588 | 2.5028 | 27.8343 | 3.8693 | 19.0586 | 27.8325 | 6.4795 | 0.0022 | 0.0036 | 0.0015 | |
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| 2.0255 | 7.0 | 4186 | 2.5544 | 8.2864 | 0.5105 | 7.6569 | 8.2655 | 3.0 | 0.0005 | 0.0008 | 0.0003 | |
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| 1.9177 | 8.0 | 4784 | 2.5356 | 22.6347 | 3.1887 | 14.2667 | 22.6751 | 7.0 | 0.0005 | 0.0008 | 0.0003 | |
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| 1.7165 | 9.0 | 5382 | 2.5492 | 9.9002 | 0.817 | 9.31 | 9.8757 | 4.0 | 0.0005 | 0.0008 | 0.0003 | |
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| 1.645 | 10.0 | 5980 | 2.6712 | 9.9002 | 0.817 | 9.31 | 9.8757 | 4.0 | 0.0005 | 0.0008 | 0.0003 | |
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### Framework versions |
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- Transformers 4.43.3 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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