transformers tensorboard safetensors t5 text2text-generation generated_from_trainer dataset:samsum base_model:google/flan-t5-small license:apache-2.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
---
license: apache-2.0
base_model: google/flan-t5-small
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: flan-t5-small-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: 42.4655
---

<!-- 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-small-samsum

This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6732
- Rouge1: 42.4655
- Rouge2: 18.4875
- Rougel: 35.2198
- Rougelsum: 38.6465
- Gen Len: 16.8486

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.8853        | 0.22  | 100  | 1.7046          | 42.3969 | 18.365  | 35.0091 | 38.6527   | 16.6703 |
| 1.8463        | 0.43  | 200  | 1.6954          | 42.5607 | 18.4425 | 35.1088 | 38.8749   | 17.3565 |
| 1.8549        | 0.65  | 300  | 1.6794          | 42.5148 | 18.4716 | 35.1769 | 38.7018   | 17.1123 |
| 1.8361        | 0.87  | 400  | 1.6775          | 42.3899 | 18.4343 | 35.134  | 38.5732   | 16.6215 |
| 1.8132        | 1.08  | 500  | 1.6732          | 42.4655 | 18.4875 | 35.2198 | 38.6465   | 16.8486 |
| 1.8073        | 1.3   | 600  | 1.6708          | 42.4741 | 18.3824 | 35.1819 | 38.6066   | 16.9475 |
| 1.7973        | 1.52  | 700  | 1.6686          | 42.8206 | 18.7011 | 35.3874 | 38.9173   | 16.7595 |
| 1.798         | 1.74  | 800  | 1.6666          | 42.7779 | 18.6627 | 35.323  | 38.9467   | 16.9389 |
| 1.79          | 1.95  | 900  | 1.6668          | 42.8071 | 18.7113 | 35.2872 | 38.8641   | 16.9426 |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0
