---
license: apache-2.0
tags:
- generated_from_trainer
- summarization
metrics:
- rouge
datasets:
- stacked-summaries/stacked-samsum-1024
model-index:
- name: flan-t5-large-stacked-samsum1024-WIP3
  results:
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: samsum
      type: samsum
      config: samsum
      split: test
    metrics:
    - name: ROUGE-1
      type: rouge
      value: 47.6682
      verified: true
    - name: ROUGE-2
      type: rouge
      value: 23.3053
      verified: true
    - name: ROUGE-L
      type: rouge
      value: 39.7678
      verified: true
    - name: ROUGE-LSUM
      type: rouge
      value: 43.259
      verified: true
    - name: loss
      type: loss
      value: 2.372586965560913
      verified: true
    - name: gen_len
      type: gen_len
      value: 17.4237
      verified: true
---


# flan-t5-large-stacked-samsum-1024

 <a href="https://colab.research.google.com/gist/pszemraj/a4bf61f593ebda9a8db6dc58839d9de4/brief-demo-flan-t5-stacked-samsum.ipynb">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>

This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the `stacked-summaries/stacked-samsum-1024` dataset.

It achieves the following results on the evaluation set:
- Loss: 2.1846
- Rouge1: 57.9637
- Rouge2: 28.7446
- Rougel: 44.3826
- Rougelsum: 54.0399
- Gen Len: 122.77

## Model description

More information needed

## Intended uses & limitations

- max input/output is 1024 tokens
- this is mostly a test because `samsum` is not exactly the best dataset for general-purpose summarization

## Training and evaluation data

See the dataset card linked on this page for info

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- seed: 24915
- distributed_type: multi-GPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.02
- num_epochs: 1.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.1195        | 0.17  | 20   | 2.0635          | 57.8829 | 28.7887 | 44.4256 | 54.1299   | 121.8   |
| 0.1084        | 0.35  | 40   | 2.1178          | 58.0416 | 28.6487 | 44.3905 | 54.1557   | 122.893 |
| 0.1019        | 0.52  | 60   | 2.1576          | 57.816  | 28.7069 | 44.4242 | 53.9598   | 120.524 |
| 0.0975        | 0.7   | 80   | 2.1821          | 57.9597 | 28.8178 | 44.4854 | 54.068    | 121.793 |
| 0.0947        | 0.87  | 100  | 2.1846          | 57.9637 | 28.7446 | 44.3826 | 54.0399   | 122.77  |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.6.1
- Tokenizers 0.13.1