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--- |
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base_model: google/pegasus-xsum |
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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: LLM_Teached_Pegasus_50k |
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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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# LLM_Teached_Pegasus_50k |
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This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6378 |
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- Rouge1: 0.4698 |
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- Rouge2: 0.2197 |
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- Rougel: 0.385 |
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- Rougelsum: 0.3849 |
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- Gen Len: 26.5251 |
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- Precision: 0.9107 |
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- Recall: 0.909 |
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- F1: 0.9096 |
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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: 32 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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: 16 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | F1 | Gen Len | Validation Loss | Precision | Recall | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:----:|:------:|:-------:|:---------------:|:---------:|:------:|:------:|:------:|:------:|:---------:| |
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| No log | 1.0 | 390 | 0.9034 | 26.2967 | 1.8258 | 0.9049 | 0.9023 | 0.4338 | 0.1906 | 0.3496 | 0.3498 | |
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| 2.1621 | 2.0 | 781 | 0.9054 | 26.2727 | 1.7537 | 0.9068 | 0.9044 | 0.4449 | 0.2005 | 0.3633 | 0.3633 | |
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| 1.8794 | 3.0 | 1172 | 0.9066 | 26.4345 | 1.7268 | 0.9078 | 0.9058 | 0.4518 | 0.2061 | 0.3696 | 0.3695 | |
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| 1.8271 | 4.0 | 1560 | 0.9069 | 26.3971 | 1.7157 | 0.9082 | 0.906 | 0.4539 | 0.2075 | 0.3716 | 0.3714 | |
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| 1.8271 | 5.0 | 1951 | 0.9074 | 26.3015 | 1.7033 | 0.9087 | 0.9065 | 0.4561 | 0.2098 | 0.3735 | 0.3734 | |
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| 1.8067 | 6.0 | 2340 | 0.9077 | 26.4389 | 1.6897 | 0.9089 | 0.9069 | 0.4592 | 0.2114 | 0.3762 | 0.3759 | |
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| 1.7833 | 7.0 | 2731 | 0.9079 | 26.3745 | 1.6819 | 0.9092 | 0.9071 | 0.4598 | 0.2115 | 0.3764 | 0.376 | |
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| 1.7683 | 8.0 | 3120 | 0.9083 | 26.6204 | 1.6763 | 0.9094 | 0.9076 | 0.4621 | 0.2133 | 0.3791 | 0.3789 | |
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| 1.7559 | 9.0 | 3511 | 0.9086 | 26.424 | 1.6662 | 0.9098 | 0.9078 | 0.4632 | 0.215 | 0.38 | 0.3799 | |
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| 1.7559 | 10.0 | 3902 | 0.9089 | 26.5425 | 1.6594 | 0.9099 | 0.9082 | 0.4651 | 0.2168 | 0.3812 | 0.3812 | |
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| 1.7357 | 11.0 | 4293 | 0.9091 | 26.6051 | 1.6555 | 0.91 | 0.9086 | 0.4663 | 0.2178 | 0.3824 | 0.3823 | |
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| 1.7297 | 12.0 | 4680 | 1.6508 | 0.4668 | 0.2175 | 0.3823 | 0.3822 | 26.4393| 0.9103 | 0.9084 | 0.9092 | |
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| 1.7165 | 13.0 | 5071 | 1.6451 | 0.4687 | 0.2191 | 0.3834 | 0.3834 | 26.6385| 0.9103 | 0.9089 | 0.9094 | |
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| 1.7165 | 14.0 | 5462 | 1.6405 | 0.4691 | 0.2193 | 0.3845 | 0.3844 | 26.4156| 0.9106 | 0.9087 | 0.9095 | |
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| 1.7068 | 15.0 | 5853 | 1.6383 | 0.4699 | 0.2204 | 0.3853 | 0.3853 | 26.4571| 0.9108 | 0.9089 | 0.9097 | |
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| 1.7004 | 15.99 | 6240 | 1.6378 | 0.4698 | 0.2197 | 0.385 | 0.3849 | 26.5251| 0.9107 | 0.909 | 0.9096 | |
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### Framework versions |
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- Transformers 4.36.0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.5 |
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- Tokenizers 0.15.0 |
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