transformers pytorch longt5 text2text-generation generated_from_trainer summarization en dataset:pszemraj/qmsum-cleaned license:apache-2.0 autotrain_compatible region:us
---
datasets:
- pszemraj/qmsum-cleaned
language:
- en
license: apache-2.0
metrics:
- rouge
pipeline_tag: summarization
tags:
- generated_from_trainer
inference: false
---

# long-t5-tglobal-xl-qmsum-wip

> ⚠️ warning - this is a work in progress ⚠️

<a href="https://colab.research.google.com/gist/pszemraj/ea0ac20dae4ad84bea4ea64543f84a85/long-t5-tglobal-xl-qmsum-wip.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/long-t5-tglobal-xl](https://huggingface.co/google/long-t5-tglobal-xl) on the `pszemraj/qmsum-cleaned` dataset. 
- Refer to the [dataset card](https://huggingface.co/datasets/pszemraj/qmsum-cleaned) for details but this model was trained **with the task/prompt prefixes at the start of `input`** which means that **inference should be run in a similar fashion**.
- an example of how to run inference is in the Colab notebook linked above.

It achieves the following results on the evaluation set:
- Loss: 2.0505
- Rouge1: 35.3881
- Rouge2: 11.509
- Rougel: 23.1543
- Rougelsum: 31.3295
- Gen Len: 80.8

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 2526
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.5376        | 1.0   | 99   | 2.0104          | 35.8802 | 11.4595 | 23.6656 | 31.49     | 77.77   |
| 1.499         | 2.0   | 198  | 2.0358          | 35.1265 | 11.549  | 23.1062 | 30.8815   | 88.88   |
| 1.5034        | 3.0   | 297  | 2.0505          | 35.3881 | 11.509  | 23.1543 | 31.3295   | 80.8    |