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---
tags:
- generated_from_trainer
datasets:
- pub_med_summarization_dataset
metrics:
- rouge
model-index:
- name: pegasus-large-finetuned-Pubmed
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: pub_med_summarization_dataset
type: pub_med_summarization_dataset
args: document
metrics:
- name: Rouge1
type: rouge
value: 39.1107
---
<!-- 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-large-finetuned-Pubmed
This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on the pub_med_summarization_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7669
- Rouge1: 39.1107
- Rouge2: 15.4127
- Rougel: 24.3729
- Rougelsum: 35.1236
- Gen Len: 226.594
## 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 2.065 | 1.0 | 1000 | 1.8262 | 37.1986 | 14.3685 | 23.7153 | 33.0713 | 218.902 |
| 1.9552 | 2.0 | 2000 | 1.7933 | 38.0663 | 14.7813 | 23.8412 | 33.9574 | 217.488 |
| 1.8983 | 3.0 | 3000 | 1.7768 | 38.3975 | 15.0983 | 24.0247 | 34.314 | 222.32 |
| 1.882 | 4.0 | 4000 | 1.7687 | 39.1311 | 15.4167 | 24.2978 | 35.078 | 222.564 |
| 1.8456 | 5.0 | 5000 | 1.7669 | 39.1107 | 15.4127 | 24.3729 | 35.1236 | 226.594 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.11.6
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