transformers tensorboard safetensors t5 text2text-generation generated_from_trainer base_model:google-t5/t5-small base_model:finetune:google-t5/t5-small license:apache-2.0 autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model: google-t5/t5-small
license: apache-2.0
metrics:
- rouge
tags:
- generated_from_trainer
model-index:
- name: summarization_model
  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. -->

# summarization_model

This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5805
- Rouge1: 0.1786
- Rouge2: 0.0576
- Rougel: 0.1488
- Rougelsum: 0.148
- Gen Len: 18.5642

## 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: 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: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log        | 1.0   | 377  | 1.6488          | 0.1711 | 0.0545 | 0.1441 | 0.1445    | 18.5134 |
| 1.851         | 2.0   | 754  | 1.6059          | 0.1743 | 0.0565 | 0.1478 | 0.1477    | 18.5134 |
| 1.7899        | 3.0   | 1131 | 1.5859          | 0.1758 | 0.0575 | 0.1463 | 0.1459    | 18.5433 |
| 1.7524        | 4.0   | 1508 | 1.5805          | 0.1786 | 0.0576 | 0.1488 | 0.148     | 18.5642 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
