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--- |
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base_model: google/pegasus-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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- 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_FS |
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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_FS |
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This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6167 |
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- Rouge1: 0.4649 |
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- Rouge2: 0.2096 |
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- Rougel: 0.3686 |
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- Rougelsum: 0.3688 |
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- Gen Len: 30.6191 |
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- Precision: 0.9102 |
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- Recall: 0.9083 |
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- F1: 0.9091 |
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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: 24 |
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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: 96 |
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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 | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:---------:|:------:|:------:| |
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| No log | 1.0 | 208 | 1.8075 | 0.411 | 0.1689 | 0.3152 | 0.3155 | 29.9091 | 0.901 | 0.897 | 0.8988 | |
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| No log | 2.0 | 417 | 1.7312 | 0.4379 | 0.1893 | 0.3442 | 0.3446 | 29.9073 | 0.9059 | 0.9024 | 0.904 | |
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| 2.0112 | 3.0 | 625 | 1.6987 | 0.4475 | 0.1978 | 0.352 | 0.3525 | 30.0173 | 0.9075 | 0.9039 | 0.9055 | |
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| 2.0112 | 4.0 | 834 | 1.6768 | 0.4514 | 0.1981 | 0.357 | 0.3573 | 30.0618 | 0.9082 | 0.9047 | 0.9063 | |
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| 1.7647 | 5.0 | 1042 | 1.6617 | 0.4537 | 0.2003 | 0.3592 | 0.3595 | 30.3264 | 0.9084 | 0.9055 | 0.9068 | |
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| 1.7647 | 6.0 | 1251 | 1.6502 | 0.4554 | 0.2021 | 0.3607 | 0.361 | 30.0827 | 0.9089 | 0.9057 | 0.9072 | |
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| 1.7647 | 7.0 | 1459 | 1.6416 | 0.4592 | 0.2052 | 0.3639 | 0.3641 | 30.0218 | 0.9099 | 0.9064 | 0.908 | |
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| 1.6948 | 8.0 | 1668 | 1.6360 | 0.4612 | 0.2054 | 0.3649 | 0.365 | 30.7827 | 0.909 | 0.9074 | 0.9081 | |
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| 1.6948 | 9.0 | 1876 | 1.6302 | 0.4621 | 0.2062 | 0.3645 | 0.3647 | 30.6291 | 0.9095 | 0.9074 | 0.9083 | |
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| 1.6501 | 10.0 | 2085 | 1.6265 | 0.4606 | 0.2051 | 0.3651 | 0.3655 | 30.4818 | 0.9095 | 0.9073 | 0.9083 | |
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| 1.6501 | 11.0 | 2293 | 1.6230 | 0.4625 | 0.2073 | 0.3658 | 0.366 | 30.8064 | 0.9097 | 0.908 | 0.9087 | |
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| 1.6222 | 12.0 | 2502 | 1.6205 | 0.4644 | 0.2082 | 0.3674 | 0.3679 | 30.5527 | 0.9103 | 0.9081 | 0.909 | |
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| 1.6222 | 13.0 | 2710 | 1.6188 | 0.4648 | 0.2087 | 0.3681 | 0.3683 | 30.8055 | 0.9101 | 0.9083 | 0.909 | |
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| 1.6222 | 14.0 | 2919 | 1.6172 | 0.4654 | 0.2097 | 0.3685 | 0.3689 | 30.6709 | 0.9104 | 0.9084 | 0.9093 | |
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| 1.6048 | 15.0 | 3127 | 1.6169 | 0.465 | 0.21 | 0.3693 | 0.3697 | 30.6309 | 0.9104 | 0.9084 | 0.9093 | |
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| 1.6048 | 15.96 | 3328 | 1.6167 | 0.4649 | 0.2096 | 0.3686 | 0.3688 | 30.6191 | 0.9102 | 0.9083 | 0.9091 | |
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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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