metadata
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
metrics:
- rouge
model-index:
- name: cnn_dailymail_t5_small
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: cnn_dailymail
type: cnn_dailymail
config: default
split: train
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.2321
cnn_dailymail_t5_small
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: 1.7271
- Rouge1: 0.2321
- Rouge2: 0.0955
- Rougel: 0.1887
- Rougelsum: 0.1887
- Gen Len: 18.9998
Model description
Text-To-Text Transfer Transformer (T5) T5-Small is the checkpoint with 60 million parameters.
Intended uses & limitations
This is an exercise for finetuning of pretrained t5 model.
Training and evaluation data
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 1.9158 | 1.0 | 10000 | 1.7333 | 0.2313 | 0.0948 | 0.1879 | 0.1879 | 18.9998 |
| 1.9316 | 2.0 | 20000 | 1.7271 | 0.2321 | 0.0955 | 0.1887 | 0.1887 | 18.9998 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3