transformers pytorch tensorboard t5 text2text-generation summarization generated_from_trainer dataset:samsum license:apache-2.0 model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
datasets:
- samsum
license: apache-2.0
metrics:
- rouge
tags:
- summarization
- generated_from_trainer
model-index:
- name: t5-v1_1-small-finetuned-samsum
  results:
  - task:
      type: text2text-generation
      name: Sequence-to-sequence Language Modeling
    dataset:
      name: samsum
      type: samsum
      config: samsum
      split: train
      args: samsum
    metrics:
    - type: rouge
      value: 0.40608242084369006
      name: Rouge1
---

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# t5-v1_1-small-finetuned-samsum

This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0053
- Rouge1: 0.4061
- Rouge2: 0.1804
- Rougel: 0.3478
- Rougelsum: 0.3774

## 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 3.9788        | 1.0   | 1842 | 2.2499          | 0.3743 | 0.1569 | 0.3191 | 0.3486    |
| 2.9091        | 2.0   | 3684 | 2.1052          | 0.3875 | 0.1680 | 0.3329 | 0.3607    |
| 2.6807        | 3.0   | 5526 | 2.0270          | 0.4009 | 0.1778 | 0.3439 | 0.3734    |
| 2.5917        | 4.0   | 7368 | 2.0053          | 0.4061 | 0.1804 | 0.3478 | 0.3774    |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
