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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
- bleu
model-index:
- name: T5-small_finetuned_billsum_subset_model_bs32_lr2e-05
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: 0.1887
- name: Bleu
type: bleu
value: 0.0008
---
<!-- 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. -->
# T5-small_finetuned_billsum_subset_model_bs32_lr2e-05
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: 1.9763
- Rouge1: 0.1887
- Rouge2: 0.0967
- Rougel: 0.1659
- Rougelsum: 0.1657
- Gen Len: 19.0
- Bleu: 0.0008
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Bleu |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:------:|
| No log | 1.0 | 31 | 1.9837 | 0.1873 | 0.0945 | 0.1635 | 0.1633 | 19.0 | 0.0007 |
| No log | 2.0 | 62 | 1.9812 | 0.1884 | 0.0955 | 0.1652 | 0.1648 | 19.0 | 0.0007 |
| No log | 3.0 | 93 | 1.9785 | 0.1866 | 0.0936 | 0.1636 | 0.1634 | 19.0 | 0.0007 |
| No log | 4.0 | 124 | 1.9763 | 0.1887 | 0.0967 | 0.1659 | 0.1657 | 19.0 | 0.0008 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
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