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---
base_model: google/pegasus-xsum
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
- precision
- recall
- f1
model-index:
- name: Pegasus_xsum_samsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: samsum
type: samsum
config: samsum
split: validation
args: samsum
metrics:
- name: Rouge1
type: rouge
value: 0.5072
- name: Precision
type: precision
value: 0.9247
- name: Recall
type: recall
value: 0.9099
- name: F1
type: f1
value: 0.917
---
<!-- 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_samsum
This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4709
- Rouge1: 0.5072
- Rouge2: 0.2631
- Rougel: 0.4243
- Rougelsum: 0.4244
- Gen Len: 19.1479
- Precision: 0.9247
- Recall: 0.9099
- F1: 0.917
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:---------:|:------:|:------:|
| 1.9542 | 1.0 | 920 | 1.5350 | 0.4928 | 0.2436 | 0.4085 | 0.4086 | 18.5672 | 0.9229 | 0.9074 | 0.9149 |
| 1.6331 | 2.0 | 1841 | 1.4914 | 0.5037 | 0.257 | 0.4202 | 0.4206 | 18.8154 | 0.9246 | 0.9092 | 0.9166 |
| 1.5694 | 3.0 | 2762 | 1.4761 | 0.5071 | 0.259 | 0.4212 | 0.4214 | 19.4487 | 0.9241 | 0.9103 | 0.917 |
| 1.5374 | 4.0 | 3680 | 1.4709 | 0.5072 | 0.2631 | 0.4243 | 0.4244 | 19.1479 | 0.9247 | 0.9099 | 0.917 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.15.0
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