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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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- bleu |
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model-index: |
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- name: Social_Principal_PegasusLargeModel |
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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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# Social_Principal_PegasusLargeModel |
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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: 5.7293 |
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- Rouge1: 42.5669 |
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- Rouge2: 11.7234 |
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- Rougel: 27.7786 |
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- Rougelsum: 39.618 |
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- Bertscore Precision: 77.477 |
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- Bertscore Recall: 80.953 |
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- Bertscore F1: 79.1718 |
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- Bleu: 0.0783 |
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- Gen Len: 193.5630 |
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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: 5e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bertscore Precision | Bertscore Recall | Bertscore F1 | Bleu | Gen Len | |
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|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------------------:|:----------------:|:------------:|:------:|:--------:| |
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| 6.8026 | 0.1314 | 100 | 6.4616 | 31.6892 | 6.7933 | 21.4758 | 29.2175 | 74.4637 | 79.3128 | 76.8049 | 0.0458 | 193.5630 | |
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| 6.3765 | 0.2628 | 200 | 6.1464 | 36.2413 | 9.2779 | 25.1904 | 33.7712 | 75.7773 | 80.0271 | 77.837 | 0.0618 | 193.5630 | |
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| 6.2227 | 0.3943 | 300 | 6.0317 | 39.5034 | 10.6245 | 26.455 | 36.7119 | 76.6932 | 80.4692 | 78.5301 | 0.0712 | 193.5630 | |
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| 6.125 | 0.5257 | 400 | 5.9252 | 39.3613 | 10.6879 | 26.5658 | 36.6574 | 76.7781 | 80.5732 | 78.6239 | 0.0723 | 193.5630 | |
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| 5.9687 | 0.6571 | 500 | 5.8381 | 40.3723 | 10.95 | 26.9783 | 37.6384 | 76.8811 | 80.6213 | 78.7007 | 0.0733 | 193.5630 | |
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| 5.9196 | 0.7885 | 600 | 5.7625 | 41.4841 | 11.4949 | 27.417 | 38.6113 | 77.0166 | 80.822 | 78.8674 | 0.0773 | 193.5630 | |
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| 5.898 | 0.9199 | 700 | 5.7293 | 42.5669 | 11.7234 | 27.7786 | 39.618 | 77.477 | 80.953 | 79.1718 | 0.0783 | 193.5630 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.2.1 |
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- Tokenizers 0.19.1 |
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