metadata
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
metrics:
- rouge
model-index:
- name: cnn_news_summary_model_trained_on_reduced_data
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: cnn_dailymail
type: cnn_dailymail
config: 3.0.0
split: train[:2%]
args: 3.0.0
metrics:
- name: Rouge1
type: rouge
value: 0.2162
cnn_news_summary_model_trained_on_reduced_data
This model is a fine-tuned version of t5-small on the cnn_dailymail dataset. It achieves the following results on the evaluation set:
- Loss: 1.6625
- Rouge1: 0.2162
- Rouge2: 0.0943
- Rougel: 0.183
- Rougelsum: 0.183
- Generated Length: 19.0
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: 2
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Generated Length |
---|---|---|---|---|---|---|---|---|
No log | 1.0 | 288 | 1.6773 | 0.2168 | 0.0946 | 0.1835 | 0.1836 | 19.0 |
1.9303 | 2.0 | 576 | 1.6625 | 0.2162 | 0.0943 | 0.183 | 0.183 | 19.0 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0