---
license: apache-2.0
base_model: google-t5/t5-large
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-large-coedit
  results: []
---

<!-- 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-large-coedit

This model is a fine-tuned version of [google-t5/t5-large](https://huggingface.co/google-t5/t5-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5679
- Rouge1: 0.6412
- Rouge2: 0.5082
- Rougel: 0.6068
- Rougelsum: 0.6066
- Sacreblue: 25.9478
- Memory Used: 4111.5
- Cuda Allocated: 2814.4805
- Cuda Reserved: 2816.0
- Ram Usage: 3545.0898
- Em: 0.0333
- Gen Len: 17.2363

## 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: 50
- eval_batch_size: 50
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 200
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Sacreblue | Memory Used | Cuda Allocated | Cuda Reserved | Ram Usage | Em     | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:---------:|:-----------:|:--------------:|:-------------:|:---------:|:------:|:-------:|
| 3.898         | 0.16  | 50   | 0.7311          | 0.3939 | 0.3011 | 0.3707 | 0.3708    | 10.1387   | 4111.5      | 2814.4805      | 2816.0        | 3545.0898 | 0.0014 | 13.4078 |
| 0.5752        | 0.31  | 100  | 0.6169          | 0.6336 | 0.4988 | 0.5994 | 0.5993    | 25.1341   | 4111.5      | 2814.4805      | 2816.0        | 3545.0898 | 0.0169 | 17.2158 |
| 0.5095        | 0.47  | 150  | 0.5912          | 0.6369 | 0.5033 | 0.6026 | 0.6026    | 25.5313   | 4111.5      | 2814.4805      | 2816.0        | 3545.0898 | 0.0256 | 17.2322 |
| 0.4836        | 0.63  | 200  | 0.5777          | 0.6398 | 0.5061 | 0.6053 | 0.6052    | 25.7757   | 4111.5      | 2814.4805      | 2816.0        | 3545.0898 | 0.0297 | 17.235  |
| 0.4634        | 0.78  | 250  | 0.5709          | 0.6411 | 0.5077 | 0.6067 | 0.6066    | 25.9025   | 4111.5      | 2814.4805      | 2816.0        | 3545.0898 | 0.0315 | 17.2362 |
| 0.4568        | 0.94  | 300  | 0.5679          | 0.6412 | 0.5082 | 0.6068 | 0.6066    | 25.9478   | 4111.5      | 2814.4805      | 2816.0        | 3545.0898 | 0.0333 | 17.2363 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2