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--- |
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library_name: peft |
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license: apache-2.0 |
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base_model: google/roberta2roberta_L-24_cnn_daily_mail |
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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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model-index: |
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- name: results |
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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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# results |
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This model is a fine-tuned version of [google/roberta2roberta_L-24_cnn_daily_mail](https://huggingface.co/google/roberta2roberta_L-24_cnn_daily_mail) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 6.7395 |
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- Rouge1: 33.11 |
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- Rouge2: 20.39 |
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- Rougel: 27.32 |
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- Rougelsum: 27.42 |
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- Bertscore P: 87.57 |
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- Bertscore R: 83.35 |
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- Bertscore F1: 85.27 |
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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: 3e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 8 |
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- optimizer: Use OptimizerNames.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: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bertscore P | Bertscore R | Bertscore F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-----------:|:-----------:|:------------:| |
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| 36.5558 | 0.8 | 20 | 7.8969 | 32.35 | 20.37 | 27.74 | 27.82 | 87.74 | 83.51 | 85.43 | |
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| 32.2661 | 1.6 | 40 | 7.0747 | 34.94 | 21.86 | 29.83 | 30.04 | 87.7 | 83.93 | 85.62 | |
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| 30.3129 | 2.4 | 60 | 6.7395 | 33.11 | 20.39 | 27.32 | 27.42 | 87.57 | 83.35 | 85.27 | |
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### Framework versions |
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- PEFT 0.14.0 |
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- Transformers 4.48.3 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.3.0 |
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- Tokenizers 0.21.0 |
