---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- pszemraj/govreport-summarization-8192
metrics:
- rouge
pipeline_tag: summarization
base_model: allenai/led-base-16384
model-index:
- name: led-base-16384-finetuned-govreport
  results:
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: pszemraj/govreport-summarization-8192
      type: pszemraj/govreport-summarization-8192
      config: split
      split: validation
      args: split
    metrics:
    - type: rouge
      value: 50.3574
      name: ROUGE-1
    - type: rouge
      value: 20.0448
      name: ROUGE-2
    - type: rouge
      value: 22.2156
      name: ROUGE-L
    - type: rouge
      value: 22.2156
      name: ROUGE-LSUM
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: pszemraj/govreport-summarization-8192
      type: pszemraj/govreport-summarization-8192
      config: split
      split: test
      args: split
    metrics:
    - type: rouge
      value: 52.6378
      name: ROUGE-1
    - type: rouge
      value: 22.213
      name: ROUGE-2
    - type: rouge
      value: 23.5898
      name: ROUGE-L
    - type: rouge
      value: 23.5898
      name: ROUGE-LSUM
---

# led-base-16384-finetuned-govreport

This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the [pszemraj/govreport-summarization-8192](https://huggingface.co/datasets/pszemraj/govreport-summarization-8192) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2887

The rouge metrics calculations were processed later down the line (final notebook can be found [HERE](https://www.kaggle.com/code/marcoloureno/led-base-16384-finetuned-govreport-metrics/notebook)).

It achieved the following results on the validation set:
- Rouge1: 50.3574
- Rouge2: 20.0448
- Rougel: 22.2156
- Rougelsum: 22.2156

It achieved the following results on the test set:
- Rouge1: 52.6378
- Rouge2: 22.2130
- Rougel: 23.5898
- Rougelsum: 23.5898


## Model description

As described in [Longformer: The Long-Document Transformer](https://arxiv.org/pdf/2004.05150.pdf) by Iz Beltagy, Matthew E. Peters, Arman Cohan, [Allenai's Longformer Encoder-Decoder (LED)](https://github.com/allenai/longformer#longformer) was initialized from [*bart-base*](https://huggingface.co/facebook/bart-base) since both models share the exact same architecture. To be able to process 16K tokens, *bart-base*'s position embedding matrix was simply copied 16 times.

This model is especially interesting for long-range summarization and question answering.

## Intended uses & limitations

[pszemraj/govreport-summarization-8192](https://huggingface.co/datasets/pszemraj/govreport-summarization-8192) is a pre-processed version of the dataset [ccdv/govreport-summarization](https://huggingface.co/datasets/ccdv/govreport-summarization), which is a dataset for summarization of long documents adapted from this [repository](https://github.com/luyang-huang96/LongDocSum) and this [paper](https://arxiv.org/pdf/2104.02112.pdf).

The Allenai's LED model was fine-tuned to this dataset, allowing the summarization of documents up to 16384 tokens.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.1492        | 0.24  | 250  | 1.4233          |
| 1.0077        | 0.49  | 500  | 1.3813          |
| 1.0069        | 0.73  | 750  | 1.3499          |
| 0.9639        | 0.98  | 1000 | 1.3216          |
| 0.7996        | 1.22  | 1250 | 1.3172          |
| 0.9395        | 1.46  | 1500 | 1.3003          |
| 0.913         | 1.71  | 1750 | 1.2919          |
| 0.8843        | 1.95  | 2000 | 1.2887          |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3