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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: my_awesome_billsum_model
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: 19.4885
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_billsum_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1303
- Rouge1: 19.4885
- Rouge2: 9.7756
- Rougel: 16.7539
- Rougelsum: 18.153
## 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: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 2.9287 | 1.0 | 124 | 2.3690 | 18.7685 | 8.721 | 15.7134 | 17.2109 |
| 2.5051 | 2.0 | 248 | 2.2540 | 19.5651 | 9.5886 | 16.5619 | 18.1252 |
| 2.4042 | 3.0 | 372 | 2.2140 | 19.4716 | 9.7429 | 16.6675 | 18.0006 |
| 2.3442 | 4.0 | 496 | 2.1800 | 19.5841 | 9.7078 | 16.7923 | 18.1682 |
| 2.3075 | 5.0 | 620 | 2.1562 | 19.4162 | 9.6647 | 16.5106 | 17.9637 |
| 2.2693 | 6.0 | 744 | 2.1394 | 19.5064 | 9.8462 | 16.6515 | 18.0461 |
| 2.2714 | 7.0 | 868 | 2.1321 | 19.475 | 9.7216 | 16.6698 | 18.1103 |
| 2.2413 | 8.0 | 992 | 2.1303 | 19.4885 | 9.7756 | 16.7539 | 18.153 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
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