metadata
tags:
- generated_from_trainer
datasets:
- orange_sum
metrics:
- rouge
model-index:
- name: bert2gpt2SUMM-finetuned-mlsum-finetuned-mlorange_sum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: orange_sum
type: orange_sum
args: abstract
metrics:
- name: Rouge1
type: rouge
value: 24.949
bert2gpt2SUMM-finetuned-mlsum-finetuned-mlorange_sum
This model is a fine-tuned version of Chemsseddine/bert2gpt2SUMM-finetuned-mlsum on the orange_sum dataset. It achieves the following results on the evaluation set:
- Loss: 3.1773
- Rouge1: 24.949
- Rouge2: 7.851
- Rougel: 18.1575
- Rougelsum: 18.4114
- Gen Len: 39.7947
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 3.5484 | 1.0 | 1338 | 3.1773 | 24.949 | 7.851 | 18.1575 | 18.4114 | 39.7947 |
Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1