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---
license: apache-2.0
tags:
- generated_from_trainer
- summarization
- stacked summaries
- prompt engineering
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
language:
- en
library_name: transformers
pipeline_tag: summarization
---
# 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 |