transformers safetensors longt5 text2text-generation summarization generated_from_trainer dataset:gov_report_summarization_dataset base_model:google/long-t5-tglobal-base base_model:finetune:google/long-t5-tglobal-base license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: google/long-t5-tglobal-base
datasets:
- gov_report_summarization_dataset
license: apache-2.0
metrics:
- rouge
pipeline_tag: summarization
tags:
- summarization
- generated_from_trainer
model-index:
- name: long-t5-tglobal-base-finetuned-govReport-4096
  results:
  - task:
      type: text2text-generation
      name: Sequence-to-sequence Language Modeling
    dataset:
      name: gov_report_summarization_dataset
      type: gov_report_summarization_dataset
      config: document
      split: validation
      args: document
    metrics:
    - type: rouge
      value: 0.0432
      name: Rouge1
---

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# long-t5-tglobal-base-finetuned-govReport-4096

This model is a fine-tuned version of [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) on the gov_report_summarization_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4052
- Rouge1: 0.0432
- Rouge2: 0.0217
- Rougel: 0.0378
- Rougelsum: 0.0408

## 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: 4e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 15.9484       | 0.99  | 31   | 2.7412          | 0.0382 | 0.0142 | 0.0319 | 0.0354    |
| 3.0143        | 1.98  | 62   | 1.7096          | 0.0385 | 0.0144 | 0.032  | 0.0355    |
| 2.1893        | 2.98  | 93   | 1.4976          | 0.0376 | 0.0138 | 0.0313 | 0.0347    |
| 1.6128        | 4.0   | 125  | 1.4406          | 0.041  | 0.0174 | 0.0354 | 0.0387    |
| 1.5438        | 4.99  | 156  | 1.4292          | 0.043  | 0.0203 | 0.0368 | 0.0408    |
| 1.5015        | 5.98  | 187  | 1.4220          | 0.0427 | 0.0205 | 0.0367 | 0.0405    |
| 1.4723        | 6.98  | 218  | 1.4071          | 0.0431 | 0.0215 | 0.0376 | 0.0408    |
| 1.4707        | 8.0   | 250  | 1.4089          | 0.0427 | 0.0212 | 0.0373 | 0.0405    |
| 1.4447        | 8.99  | 281  | 1.4046          | 0.0431 | 0.0216 | 0.0379 | 0.0408    |
| 1.4884        | 9.92  | 310  | 1.4052          | 0.0432 | 0.0217 | 0.0378 | 0.0408    |


### Framework versions

- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1