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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: 6.0725 |
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- Rouge1: 46.2376 |
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- Rouge2: 21.4997 |
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- Rougel: 39.6036 |
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- Rougelsum: 46.3269 |
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- Gen Len: 4.2121 |
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- F1: 0.3205 |
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- Recall: 0.6757 |
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- Precision: 0.2101 |
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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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| No log | 1.0 | 50 | 3.1330 | 38.9228 | 19.8713 | 34.8665 | 39.0223 | 3.6162 | 0.3373 | 0.5957 | 0.2353 | |
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| No log | 2.0 | 100 | 3.2460 | 42.6051 | 20.0714 | 38.2234 | 42.823 | 3.9697 | 0.3275 | 0.5385 | 0.2353 | |
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| No log | 3.0 | 150 | 3.4413 | 42.2575 | 19.3868 | 36.9508 | 42.1996 | 4.1313 | 0.3415 | 0.6222 | 0.2353 | |
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| 1.9251 | 4.0 | 200 | 3.6553 | 41.9902 | 19.8751 | 36.961 | 42.1914 | 3.9899 | 0.3522 | 0.7 | 0.2353 | |
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| 1.9251 | 5.0 | 250 | 3.9188 | 41.6177 | 19.8385 | 36.9836 | 41.7831 | 4.0404 | 0.3648 | 0.725 | 0.2437 | |
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| 1.9251 | 6.0 | 300 | 4.0309 | 40.2818 | 15.9608 | 35.0963 | 40.3224 | 4.3838 | 0.3522 | 0.7 | 0.2353 | |
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| 1.9251 | 7.0 | 350 | 4.4151 | 40.1585 | 14.4247 | 34.3216 | 40.2886 | 4.3131 | 0.1185 | 0.5 | 0.0672 | |
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| 0.6344 | 8.0 | 400 | 4.9239 | 42.9643 | 19.2829 | 36.6803 | 43.0145 | 4.4646 | 0.3097 | 0.6667 | 0.2017 | |
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| 0.6344 | 9.0 | 450 | 5.9057 | 45.7386 | 21.5407 | 39.3743 | 45.7904 | 4.5253 | 0.3205 | 0.6757 | 0.2101 | |
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| 0.6344 | 10.0 | 500 | 6.0725 | 46.2376 | 21.4997 | 39.6036 | 46.3269 | 4.2121 | 0.3205 | 0.6757 | 0.2101 | |
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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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