---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- govreport-summarization
metrics:
- rouge
model-index:
- name: led-large-16384-govreport
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: govreport-summarization
      type: govreport-summarization
      config: document
      split: validation
      args: document
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.5194151586540673
---

<!-- 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. -->

# led-large-16384-govreport

This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the govreport-summarization dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7624
- Rouge1: 0.5194
- Rouge2: 0.2107
- Rougel: 0.2437
- Rougelsum: 0.2437

## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 1.8152        | 3.65  | 500  | 1.7956          | 0.5095 | 0.2040 | 0.2382 | 0.2381    |
| 1.6981        | 3.66  | 1000 | 1.7624          | 0.5194 | 0.2107 | 0.2437 | 0.2437    |


### Framework versions

- Transformers 4.30.2
- Pytorch 1.10.0+cu102
- Datasets 2.13.1
- Tokenizers 0.13.3