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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_100k_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_100k_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: 1.4433 |
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- Rouge1: 0.4961 |
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- Rouge2: 0.2476 |
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- Rougel: 0.4155 |
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- Rougelsum: 0.4154 |
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- Gen Len: 25.8629 |
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- Precision: 0.9136 |
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- Recall: 0.914 |
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- F1: 0.9137 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 6 |
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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: 20 |
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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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| 1.781 | 2.0 | 1388 | 0.9088 | 26.8891 | 1.5797 | 0.908 | 0.91 | 0.4708 | 0.2219 | 0.3892 | 0.389 | |
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| 1.6618 | 3.0 | 2083 | 0.91 | 26.7282 | 1.5411 | 0.9094 | 0.9111 | 0.4776 | 0.2303 | 0.3977 | 0.3973 | |
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| 1.626 | 4.0 | 2776 | 0.911 | 26.7596 | 1.5171 | 0.9102 | 0.9121 | 0.4834 | 0.2345 | 0.402 | 0.402 | |
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| 1.5918 | 5.0 | 3471 | 0.9112 | 26.6476 | 1.5001 | 0.9106 | 0.9122 | 0.4853 | 0.2365 | 0.4045 | 0.4045 | |
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| 1.5586 | 6.0 | 4164 | 0.9116 | 26.7778 | 1.4880 | 0.9108 | 0.9127 | 0.4875 | 0.2373 | 0.4063 | 0.4063 | |
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| 1.5375 | 7.0 | 4858 | 0.912 | 26.3991 | 1.4768 | 0.9116 | 0.9128 | 0.4898 | 0.24 | 0.4083 | 0.4083 | |
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| 1.5146 | 8.0 | 5553 | 0.9126 | 26.156 | 1.4686 | 0.9123 | 0.9133 | 0.4907 | 0.241 | 0.4088 | 0.4089 | |
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| 1.5006 | 9.0 | 6247 | 0.9127 | 26.2629 | 1.4636 | 0.9122 | 0.9135 | 0.4914 | 0.2419 | 0.4097 | 0.4099 | |
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| 1.49 | 10.0 | 6942 | 0.9127 | 26.0273 | 1.4580 | 0.9125 | 0.9133 | 0.4911 | 0.2429 | 0.4109 | 0.411 | |
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| 1.4749 | 11.0 | 7636 | 0.9131 | 26.2304 | 1.4546 | 0.9127 | 0.9138 | 0.4932 | 0.244 | 0.4121 | 0.4123 | |
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| 1.4661 | 12.0 | 8331 | 0.9132 | 25.8778 | 1.4514 | 0.9133 | 0.9136 | 0.4937 | 0.2448 | 0.4126 | 0.4127 | |
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| 1.4575 | 13.0 | 9025 | 0.9133 | 26.1151 | 1.4499 | 0.913 | 0.914 | 0.4947 | 0.2453 | 0.4139 | 0.414 | |
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| 1.4511 | 14.0 | 9720 | 0.9133 | 26.0287 | 1.4478 | 0.9131 | 0.9138 | 0.4939 | 0.2451 | 0.4133 | 0.4134 | |
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| 1.4519 | 15.0 | 10414 | 0.9133 | 25.9078 | 1.4471 | 0.9132 | 0.9137 | 0.4938 | 0.2451 | 0.4134 | 0.4134 | |
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| 1.4439 | 16.0 | 11104 | 1.4474 | 0.4942 | 0.2456 | 0.4133 | 0.4134 | 26.0345| 0.9131 | 0.9139 | 0.9133 | |
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| 1.4441 | 17.0 | 11799 | 1.4447 | 0.4945 | 0.2457 | 0.4139 | 0.414 | 25.9391| 0.9133 | 0.9138 | 0.9134 | |
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| 1.444 | 18.0 | 12493 | 1.4446 | 0.4957 | 0.2473 | 0.415 | 0.4151 | 26.0107| 0.9133 | 0.9141 | 0.9135 | |
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| 1.4375 | 19.0 | 13188 | 1.4433 | 0.4961 | 0.2473 | 0.4153 | 0.4153 | 25.8869| 0.9136 | 0.914 | 0.9136 | |
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| 1.4361 | 20.0 | 13880 | 1.4433 | 0.4961 | 0.2476 | 0.4155 | 0.4154 | 25.8629| 0.9136 | 0.914 | 0.9137 | |
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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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