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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 |
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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 |
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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: 1.6606 |
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- Rouge1: 0.4557 |
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- Rouge2: 0.2019 |
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- Rougel: 0.3603 |
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- Rougelsum: 0.3597 |
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- Gen Len: 30.8509 |
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- Precision: 0.9078 |
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- Recall: 0.9053 |
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- F1: 0.9064 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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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: 4 |
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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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| 2.0887 | 1.0 | 625 | 1.7362 | 0.4326 | 0.1871 | 0.3375 | 0.3373 | 31.2482 | 0.9035 | 0.9015 | 0.9023 | |
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| 1.8362 | 2.0 | 1250 | 1.6844 | 0.4466 | 0.1942 | 0.3511 | 0.3507 | 30.3036 | 0.9071 | 0.9032 | 0.905 | |
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| 1.7784 | 3.0 | 1875 | 1.6666 | 0.451 | 0.1992 | 0.3554 | 0.3551 | 30.7991 | 0.907 | 0.9045 | 0.9056 | |
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| 1.7261 | 4.0 | 2500 | 1.6606 | 0.4557 | 0.2019 | 0.3603 | 0.3597 | 30.8509 | 0.9078 | 0.9053 | 0.9064 | |
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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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