---
license: apache-2.0
tags:
- generated_from_trainer
- summarization
datasets:
- samsum
metrics:
- rouge
widget:
- text: >
    Olivia: Hey Carter, are you still developing that restaurant business? 
    Carter: Hi Olivia  Carter: Yes, we want to launch next month :)  Olivia:
    Next month? That's soon! Congrats :)  Carter: thanks, I'm a bit nervous but
    I seriously believe we're delivering something innovative and needed 
    Olivia: I think it's a great concept and I am sure you'll do great!  Olivia:
    I am currently involved with a new restaurant in the city centre  Carter:
    Which one? Olivia: Spicy and chilled  Carter: I heard about it :) Is it any
    good? ;)  Olivia: I love the restaurant and really like working there 
    Carter: good for you! Olivia: and here's the question - are you still
    looking for restaurant to include in your discount app?  Carter: sure, but I
    think it would be better to discuss it in person - would you like to meet
    up? Olivia: That would be great!
- type: text
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: 46.8876
language:
- en
---

<!-- 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: 1.3709
- Rouge1: 46.8876
- Rouge2: 23.2689
- Rougel: 39.5369
- Rougelsum: 43.1602
- Gen Len: 17.2027

## 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          | 46.5321 | 23.0912 | 39.4008 | 42.8993   | 17.0977 |
| 1.3534        | 2.0   | 3684 | 1.3732          | 47.1111 | 23.4456 | 39.5462 | 43.2534   | 17.4554 |
| 1.2795        | 3.0   | 5526 | 1.3709          | 46.8876 | 23.2689 | 39.5369 | 43.1602   | 17.2027 |
| 1.2313        | 4.0   | 7368 | 1.3736          | 47.4418 | 23.701  | 39.9856 | 43.6294   | 17.2198 |
| 1.1934        | 5.0   | 9210 | 1.3772          | 47.4656 | 23.9199 | 40.0284 | 43.7039   | 17.3162 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2