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--- |
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library_name: transformers |
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license: mit |
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base_model: facebook/bart-large-cnn |
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tags: |
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- summarization |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: pegasus_xsum_samsum_model_10epoch |
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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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# pegasus_xsum_samsum_model_10epoch |
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This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.5260 |
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- Model Preparation Time: 0.0066 |
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- Rouge1: 0.4165 |
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- Rouge2: 0.1911 |
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- Rougel: 0.3142 |
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- Rougelsum: 0.3143 |
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- Gen Len: 60.615 |
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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: 2 |
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- total_train_batch_size: 16 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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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 | Model Preparation Time | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:------:|:------:|:---------:|:-------:| |
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| No log | 1.0 | 200 | 1.4282 | 0.0066 | 0.4109 | 0.2008 | 0.3084 | 0.3085 | 59.755 | |
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| No log | 2.0 | 400 | 1.5080 | 0.0066 | 0.4214 | 0.2027 | 0.3175 | 0.3175 | 59.3862 | |
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| 1.2171 | 3.0 | 600 | 1.5348 | 0.0066 | 0.4093 | 0.1949 | 0.3071 | 0.307 | 60.2062 | |
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| 1.2171 | 4.0 | 800 | 1.7114 | 0.0066 | 0.4092 | 0.1928 | 0.3067 | 0.3066 | 60.38 | |
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| 0.6518 | 5.0 | 1000 | 1.8757 | 0.0066 | 0.4149 | 0.1935 | 0.3118 | 0.3117 | 59.5 | |
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| 0.6518 | 6.0 | 1200 | 2.0521 | 0.0066 | 0.4126 | 0.1902 | 0.3107 | 0.3108 | 60.335 | |
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| 0.6518 | 7.0 | 1400 | 2.1551 | 0.0066 | 0.4138 | 0.1917 | 0.3117 | 0.3115 | 60.1888 | |
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| 0.3371 | 8.0 | 1600 | 2.4051 | 0.0066 | 0.4132 | 0.1913 | 0.3116 | 0.3116 | 60.28 | |
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| 0.3371 | 9.0 | 1800 | 2.4850 | 0.0066 | 0.4146 | 0.1897 | 0.3129 | 0.3131 | 60.7375 | |
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| 0.2072 | 10.0 | 2000 | 2.5260 | 0.0066 | 0.4165 | 0.1911 | 0.3142 | 0.3143 | 60.615 | |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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