metadata
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: Text_Summarization
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: billsum
type: billsum
config: default
split: ca_test
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.1447
Text_Summarization
This model is a fine-tuned version of t5-small on the billsum dataset. It achieves the following results on the evaluation set:
- Loss: 2.5015
- Rouge1: 0.1447
- Rouge2: 0.0522
- Rougel: 0.1204
- Rougelsum: 0.1202
- 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 | 2.7888 | 0.1267 | 0.0351 | 0.1053 | 0.1051 | 19.0 |
| No log | 2.0 | 124 | 2.5770 | 0.1336 | 0.0452 | 0.1108 | 0.1107 | 19.0 |
| No log | 3.0 | 186 | 2.5178 | 0.1439 | 0.0513 | 0.1188 | 0.1185 | 19.0 |
| No log | 4.0 | 248 | 2.5015 | 0.1447 | 0.0522 | 0.1204 | 0.1202 | 19.0 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3