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--- |
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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_CNNDM_2 |
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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_CNNDM_2 |
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This model was trained from scratch on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.7016 |
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- Rouge1: 0.4651 |
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- Rouge2: 0.2076 |
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- Rougel: 0.3457 |
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- Rougelsum: 0.3459 |
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- Gen Len: 52.1582 |
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- Precision: 0.906 |
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- Recall: 0.9098 |
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- F1: 0.9077 |
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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: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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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: 10 |
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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 | 312 | 1.7705 | 0.4551 | 0.1985 | 0.335 | 0.3351 | 51.6464 | 0.9043 | 0.9073 | 0.9056 | |
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| 1.8539 | 2.0 | 625 | 1.7468 | 0.4578 | 0.2016 | 0.3394 | 0.3397 | 51.0627 | 0.9054 | 0.908 | 0.9065 | |
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| 1.8539 | 3.0 | 937 | 1.7331 | 0.4595 | 0.2019 | 0.3389 | 0.3391 | 52.9318 | 0.9039 | 0.9089 | 0.9063 | |
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| 1.7903 | 4.0 | 1250 | 1.7226 | 0.4606 | 0.2032 | 0.3406 | 0.3405 | 52.8055 | 0.9046 | 0.9094 | 0.9068 | |
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| 1.746 | 5.0 | 1562 | 1.7132 | 0.4642 | 0.2068 | 0.3453 | 0.3453 | 51.7873 | 0.9062 | 0.9096 | 0.9077 | |
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| 1.746 | 6.0 | 1875 | 1.7117 | 0.463 | 0.2055 | 0.3435 | 0.3436 | 53.4382 | 0.905 | 0.91 | 0.9073 | |
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| 1.7173 | 7.0 | 2187 | 1.7057 | 0.4644 | 0.2073 | 0.3456 | 0.3457 | 52.1718 | 0.906 | 0.9099 | 0.9078 | |
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| 1.7004 | 8.0 | 2500 | 1.7033 | 0.4668 | 0.2084 | 0.3464 | 0.3466 | 51.9 | 0.9063 | 0.91 | 0.908 | |
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| 1.7004 | 9.0 | 2812 | 1.7027 | 0.4651 | 0.2074 | 0.3457 | 0.3458 | 52.3591 | 0.906 | 0.9099 | 0.9078 | |
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| 1.6888 | 9.98 | 3120 | 1.7016 | 0.4651 | 0.2076 | 0.3457 | 0.3459 | 52.1582 | 0.906 | 0.9098 | 0.9077 | |
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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 2.7.1 |
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- Tokenizers 0.15.2 |
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