metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: 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: 0.1453
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: 2.4987
- Rouge1: 0.1453
- Rouge2: 0.0544
- Rougel: 0.1203
- 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 | 2.7907 | 0.1226 | 0.0355 | 0.1036 | 0.1034 | 19.0 |
| No log | 2.0 | 124 | 2.5804 | 0.1343 | 0.0468 | 0.1126 | 0.1125 | 19.0 |
| No log | 3.0 | 186 | 2.5159 | 0.1441 | 0.0549 | 0.1198 | 0.1197 | 19.0 |
| No log | 4.0 | 248 | 2.4987 | 0.1453 | 0.0544 | 0.1203 | 0.1202 | 19.0 |
Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3