transformers tensorboard safetensors t5 text2text-generation generated_from_trainer dataset:samsum base_model:google/flan-t5-small license:apache-2.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
---
license: apache-2.0
base_model: google/flan-t5-small
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: flan-t5-small-samsum
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: samsum
      type: samsum
      config: samsum
      split: test
      args: samsum
    metrics:
    - name: Rouge1
      type: rouge
      value: 42.6
---

<!-- 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. -->

# flan-t5-small-samsum

This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6729
- Rouge1: 42.6
- Rouge2: 18.7153
- Rougel: 35.4138
- Rougelsum: 38.8543
- Gen Len: 16.9170

## 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: 5e-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: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.8863        | 0.22  | 100  | 1.7049          | 42.0859 | 18.0002 | 34.7349 | 38.3446   | 16.5788 |
| 1.8463        | 0.43  | 200  | 1.6947          | 42.4056 | 18.3005 | 34.9821 | 38.8013   | 17.3614 |
| 1.8548        | 0.65  | 300  | 1.6792          | 42.585  | 18.5643 | 35.2235 | 38.8298   | 17.1514 |
| 1.8358        | 0.87  | 400  | 1.6772          | 42.1544 | 18.2303 | 34.8971 | 38.3609   | 16.5873 |
| 1.8129        | 1.08  | 500  | 1.6729          | 42.6    | 18.7153 | 35.4138 | 38.8543   | 16.9170 |
| 1.8068        | 1.3   | 600  | 1.6709          | 42.5217 | 18.3285 | 35.1455 | 38.5954   | 16.9451 |
| 1.7973        | 1.52  | 700  | 1.6687          | 42.8667 | 18.624  | 35.3429 | 38.9322   | 16.7546 |
| 1.7979        | 1.74  | 800  | 1.6668          | 42.919  | 18.7388 | 35.4528 | 39.0561   | 16.8791 |
| 1.7899        | 1.95  | 900  | 1.6670          | 43.0931 | 18.741  | 35.5047 | 39.2321   | 16.9109 |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0
