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---
base_model: google/pegasus-xsum
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: pegasus-xsum-finetuned-cnn_dailymail
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-xsum-finetuned-cnn_dailymail
This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8958
- Rouge1: 45.7795
- Rouge2: 23.3182
- Rougel: 32.9241
- Rougelsum: 42.3126
- Bleu 1: 35.4715
- Bleu 2: 24.0726
- Bleu 3: 17.9591
- Meteor: 32.8897
- Lungime rezumat: 43.3773
- Lungime original: 48.6937
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu 1 | Bleu 2 | Bleu 3 | Meteor | Lungime rezumat | Lungime original |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-------:|:-------:|:-------:|:---------------:|:----------------:|
| 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 |
| 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 |
| 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 |
| 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 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1
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