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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: 1.7193 |
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- Rouge1: 0.4541 |
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- Rouge2: 0.2071 |
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- Rougel: 0.3708 |
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- Rougelsum: 0.3708 |
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- Gen Len: 26.4531 |
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- Precision: 0.9082 |
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- Recall: 0.9061 |
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- F1: 0.907 |
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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: 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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| No log | 1.0 | 390 | 1.8258 | 0.4338 | 0.1906 | 0.3496 | 0.3498 | 26.2967 | 0.9049 | 0.9023 | 0.9034 | |
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| 2.1621 | 2.0 | 781 | 1.7537 | 0.4449 | 0.2005 | 0.3633 | 0.3633 | 26.2727 | 0.9068 | 0.9044 | 0.9054 | |
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| 1.8794 | 3.0 | 1172 | 1.7268 | 0.4518 | 0.2061 | 0.3696 | 0.3695 | 26.4345 | 0.9078 | 0.9058 | 0.9066 | |
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| 1.8271 | 3.99 | 1560 | 1.7193 | 0.4541 | 0.2071 | 0.3708 | 0.3708 | 26.4531 | 0.9082 | 0.9061 | 0.907 | |
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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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