metadata
license: apache-2.0
base_model: google/t5-efficient-tiny
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: billsum_tiny_summarization
results:
- task:
name: Sequence-to-sequence Language Modeling
type: summarization
dataset:
name: billsum
type: billsum
config: default
split: ca_test
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.1503
pipeline_tag: summarization
billsum_tiny_summarization
This model is a fine-tuned version of google/t5-efficient-tiny on the billsum dataset. It achieves the following results on the evaluation set:
- Loss: 3.5889
- Rouge1: 0.1503
- Rouge2: 0.0412
- Rougel: 0.1244
- Rougelsum: 0.1244
- Gen Len: 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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 62 | 4.2835 | 0.1413 | 0.0323 | 0.1125 | 0.1124 | 19.0 |
| No log | 2.0 | 124 | 3.7275 | 0.1507 | 0.0408 | 0.1263 | 0.1264 | 19.0 |
| No log | 3.0 | 186 | 3.6154 | 0.1499 | 0.0407 | 0.1244 | 0.1244 | 19.0 |
| No log | 4.0 | 248 | 3.5889 | 0.1503 | 0.0412 | 0.1244 | 0.1244 | 19.0 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3