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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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metrics: |
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- rouge |
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model-index: |
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- name: pegasus-xsum-finetuned-cnn_dailymail |
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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-finetuned-cnn_dailymail |
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This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8958 |
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- Rouge1: 45.7795 |
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- Rouge2: 23.3182 |
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- Rougel: 32.9241 |
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- Rougelsum: 42.3126 |
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- Bleu 1: 35.4715 |
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- Bleu 2: 24.0726 |
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- Bleu 3: 17.9591 |
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- Meteor: 32.8897 |
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- Lungime rezumat: 43.3773 |
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- Lungime original: 48.6937 |
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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: 5.6e-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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- 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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu 1 | Bleu 2 | Bleu 3 | Meteor | Lungime rezumat | Lungime original | |
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|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-------:|:-------:|:-------:|:---------------:|:----------------:| |
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| 1.1281 | 1.0 | 14330 | 0.9373 | 44.64 | 22.2111 | 32.0228 | 41.1223 | 34.4946 | 23.079 | 17.0673 | 31.8685 | 43.543 | 48.6937 | |
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| 0.9091 | 2.0 | 28660 | 0.9095 | 45.0713 | 22.7428 | 32.4247 | 41.554 | 34.9397 | 23.5631 | 17.5094 | 32.1814 | 43.3467 | 48.6937 | |
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| 0.8455 | 3.0 | 42990 | 0.8982 | 45.5457 | 23.1315 | 32.7153 | 42.0349 | 35.2659 | 23.8773 | 17.8174 | 32.7185 | 43.5743 | 48.6937 | |
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| 0.8076 | 4.0 | 57320 | 0.8958 | 45.7795 | 23.3182 | 32.9241 | 42.3126 | 35.4715 | 24.0726 | 17.9591 | 32.8897 | 43.3773 | 48.6937 | |
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### Framework versions |
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- Transformers 4.40.0 |
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- Pytorch 2.2.2+cu118 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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