transformers pytorch safetensors t5 text2text-generation stacked summaries samsum summarization en dataset:stacked-summaries/stacked-samsum-1024 license:apache-2.0 autotrain_compatible text-generation-inference endpoints_compatible region:us
---
datasets:
- stacked-summaries/stacked-samsum-1024
language:
- en
license: apache-2.0
metrics:
- rouge
pipeline_tag: summarization
tags:
- stacked summaries
- samsum
---


# flan-t5-small-stacked-samsum-1024

This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the `stacked-summaries/stacked-samsum-1024` dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7573
- Rouge1: 46.6072
- Rouge2: 19.9754
- Rougel: 35.2715
- Rougelsum: 43.3599
- Gen Len: 72.64

## Model Description

Trained on a summarization task with _potentially_ multiple doc-summary pairs stacked on top of each other.

You can separate its predictions by using it's special token `[NEXT_CONCEPT]` to split the output into "separate topics".

## Intended use & limitations

- This is intended to be used as a baseline/reference for comparison with the larger models.

## Training and evaluation data

See `stacked-summaries/stacked-samsum-1024`.

## Training Procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 22138
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.9011        | 1.0   | 230  | 1.7986          | 45.4597 | 19.6956 | 34.6878 | 42.3724   | 74.16   |
| 1.8297        | 2.0   | 460  | 1.7609          | 46.0427 | 20.2299 | 35.2076 | 43.0549   | 70.56   |
| 1.7637        | 3.0   | 690  | 1.7573          | 46.6072 | 19.9754 | 35.2715 | 43.3599   | 72.64   |