metadata
license: apache-2.0
base_model: google-t5/t5-small
tags:
- summarization
- generated_from_trainer
datasets:
- govreport-summarization
metrics:
- rouge
model-index:
- name: t5-small-finetuned-govReport-3072
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: govreport-summarization
type: govreport-summarization
config: document
split: validation
args: document
metrics:
- name: Rouge1
type: rouge
value: 0.0371
pipeline_tag: summarization
t5-small-finetuned-govReport-3072
This model is a fine-tuned version of google-t5/t5-small on the govreport-summarization dataset. It achieves the following results on the evaluation set:
- Loss: 3.8367
- Rouge1: 0.0371
- Rouge2: 0.0142
- Rougel: 0.0316
- Rougelsum: 0.0352
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| 19.9287 | 0.99 | 31 | 11.5775 | 0.0331 | 0.0151 | 0.0293 | 0.0317 |
| 12.489 | 1.98 | 62 | 9.1322 | 0.0373 | 0.0162 | 0.0322 | 0.0351 |
| 10.8693 | 2.98 | 93 | 7.8834 | 0.0367 | 0.0153 | 0.0327 | 0.0348 |
| 9.1603 | 4.0 | 125 | 6.8580 | 0.0374 | 0.0162 | 0.0322 | 0.0355 |
| 8.2587 | 4.99 | 156 | 5.7038 | 0.0382 | 0.0154 | 0.0326 | 0.0366 |
| 6.6869 | 5.98 | 187 | 4.8553 | 0.0388 | 0.0159 | 0.0341 | 0.037 |
| 5.8997 | 6.98 | 218 | 4.3049 | 0.0383 | 0.0145 | 0.0336 | 0.036 |
| 5.0285 | 8.0 | 250 | 3.9143 | 0.0369 | 0.0138 | 0.0311 | 0.035 |
| 4.5944 | 8.99 | 281 | 3.8533 | 0.0376 | 0.0149 | 0.032 | 0.0353 |
| 4.5239 | 9.92 | 310 | 3.8367 | 0.0371 | 0.0142 | 0.0316 | 0.0352 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1