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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: flan-t5-base-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: 47.4798
---
<!-- 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-base-samsum
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3772
- Rouge1: 47.4798
- Rouge2: 23.9756
- Rougel: 40.0392
- Rougelsum: 43.6545
- Gen Len: 17.3162
## 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: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.4403 | 1.0 | 1842 | 1.3829 | 46.5346 | 23.1326 | 39.4401 | 42.8272 | 17.0977 |
| 1.3534 | 2.0 | 3684 | 1.3732 | 47.0911 | 23.5074 | 39.5951 | 43.2279 | 17.4554 |
| 1.2795 | 3.0 | 5526 | 1.3709 | 46.8895 | 23.3243 | 39.5909 | 43.1286 | 17.2027 |
| 1.2313 | 4.0 | 7368 | 1.3736 | 47.4946 | 23.7802 | 39.9999 | 43.5903 | 17.2198 |
| 1.1934 | 5.0 | 9210 | 1.3772 | 47.4798 | 23.9756 | 40.0392 | 43.6545 | 17.3162 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
### Papers With Code Results
As of 2 February 2023 the Papers with Code page for this task has the following leaderboard.
Our score (Rouge 1 score of 47.4798) puts this model's performance between fourth and fifth place on the leaderboard:
## Model Recycling
[Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=9.04&mnli_lp=nan&20_newsgroup=3.55&ag_news=1.66&amazon_reviews_multi=0.19&anli=14.53&boolq=16.60&cb=24.91&cola=10.35&copa=25.50&dbpedia=5.73&esnli=5.31&financial_phrasebank=19.96&imdb=0.05&isear=0.59&mnli=11.74&mrpc=15.89&multirc=5.99&poem_sentiment=23.27&qnli=3.93&qqp=5.54&rotten_tomatoes=3.54&rte=23.90&sst2=-0.14&sst_5bins=5.12&stsb=20.58&trec_coarse=4.15&trec_fine=10.93&tweet_ev_emoji=12.87&tweet_ev_emotion=6.02&tweet_ev_hate=-0.04&tweet_ev_irony=7.12&tweet_ev_offensive=2.16&tweet_ev_sentiment=-0.00&wic=12.03&wnli=9.44&wsc=9.37&yahoo_answers=3.04&model_name=andreaparker%2Fflan-t5-base-samsum&base_name=google%2Ft5-v1_1-base) using andreaparker/flan-t5-base-samsum as a base model yields average score of 77.86 in comparison to 68.82 by google/t5-v1_1-base.
The model is ranked 2nd among all tested models for the google/t5-v1_1-base architecture as of 07/02/2023
Results:
| 20_newsgroup | ag_news | amazon_reviews_multi | anli | boolq | cb | cola | copa | dbpedia | esnli | financial_phrasebank | imdb | isear | mnli | mrpc | multirc | poem_sentiment | qnli | qqp | rotten_tomatoes | rte | sst2 | sst_5bins | stsb | trec_coarse | trec_fine | tweet_ev_emoji | tweet_ev_emotion | tweet_ev_hate | tweet_ev_irony | tweet_ev_offensive | tweet_ev_sentiment | wic | wnli | wsc | yahoo_answers |
|---------------:|----------:|-----------------------:|--------:|--------:|--------:|--------:|-------:|----------:|--------:|-----------------------:|-------:|--------:|--------:|--------:|----------:|-----------------:|--------:|--------:|------------------:|--------:|-------:|------------:|--------:|--------------:|------------:|-----------------:|-------------------:|----------------:|-----------------:|---------------------:|---------------------:|--------:|-------:|--------:|----------------:|
| 86.4312 | 89.8333 | 67.1 | 52.5937 | 82.1713 | 80.3571 | 80.5369 | 66 | 76.5 | 90.8897 | 86.7 | 93.044 | 71.6428 | 87.2457 | 88.7255 | 62.1287 | 91.3462 | 93.3004 | 89.1393 | 89.5872 | 84.4765 | 93.578 | 56.9683 | 89.3674 | 97.4 | 93 | 46.334 | 81.6327 | 51.4815 | 74.7449 | 84.7674 | 69.8795 | 67.8683 | 56.338 | 57.6923 | 72.3 |
For more information, see: [Model Recycling](https://ibm.github.io/model-recycling/)
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