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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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datasets: |
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- samsum |
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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: Pegasus_xsum_samsum |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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dataset: |
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name: samsum |
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type: samsum |
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config: samsum |
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split: validation |
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args: samsum |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.5072 |
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- name: Precision |
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type: precision |
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value: 0.9247 |
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- name: Recall |
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type: recall |
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value: 0.9099 |
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- name: F1 |
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type: f1 |
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value: 0.917 |
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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 |
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This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on the samsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4709 |
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- Rouge1: 0.5072 |
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- Rouge2: 0.2631 |
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- Rougel: 0.4243 |
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- Rougelsum: 0.4244 |
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- Gen Len: 19.1479 |
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- Precision: 0.9247 |
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- Recall: 0.9099 |
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- F1: 0.917 |
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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: 4 |
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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: 16 |
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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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| 1.9542 | 1.0 | 920 | 1.5350 | 0.4928 | 0.2436 | 0.4085 | 0.4086 | 18.5672 | 0.9229 | 0.9074 | 0.9149 | |
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| 1.6331 | 2.0 | 1841 | 1.4914 | 0.5037 | 0.257 | 0.4202 | 0.4206 | 18.8154 | 0.9246 | 0.9092 | 0.9166 | |
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| 1.5694 | 3.0 | 2762 | 1.4761 | 0.5071 | 0.259 | 0.4212 | 0.4214 | 19.4487 | 0.9241 | 0.9103 | 0.917 | |
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| 1.5374 | 4.0 | 3680 | 1.4709 | 0.5072 | 0.2631 | 0.4243 | 0.4244 | 19.1479 | 0.9247 | 0.9099 | 0.917 | |
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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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