metadata
tags:
- xsum_v1_last
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: pegasus-large-finetune-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
- name: Rouge1
type: rouge
value: 5.0462
pegasus-large-finetune-xsum
This model is a fine-tuned version of google/pegasus-large on the xsum dataset. It achieves the following results on the evaluation set:
- Loss: 10.0826
- Rouge1: 5.0462
- Rouge2: 0.6914
- Rougel: 3.5071
- Rougelsum: 3.9548
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| 11.4044 | 1.0 | 13 | 10.7501 | 5.5154 | 0.5561 | 3.8425 | 4.2435 |
| 10.5741 | 2.0 | 26 | 10.2309 | 5.4282 | 0.7228 | 3.5759 | 4.0538 |
| 10.0146 | 3.0 | 39 | 10.0826 | 5.0462 | 0.6914 | 3.5071 | 3.9548 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.11.0