---
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: 0.5398781387812484
---

<!-- 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: 1.7442
- Rouge1: 0.5399
- Rouge2: 0.2253
- Rougel: 0.2582
- Rougelsum: 0.2582

## 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: 30

### 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    |
| 1.7048        | 5.49  | 1500 | 1.7448          | 0.5253 | 0.2149 | 0.2467 | 0.2467    |
| 1.6469        | 7.32  | 2000 | 1.7416          | 0.5299 | 0.2177 | 0.2499 | 0.2500    |
| 1.6465        | 9.15  | 2500 | 1.7318          | 0.5299 | 0.2160 | 0.2476 | 0.2478    |
| 1.578         | 10.98 | 3000 | 1.7254          | 0.5321 | 0.2192 | 0.2529 | 0.2530    |
| 1.5631        | 12.81 | 3500 | 1.7189          | 0.5309 | 0.2170 | 0.2520 | 0.2520    |
| 1.5641        | 14.63 | 4000 | 1.7152          | 0.5343 | 0.2198 | 0.2550 | 0.2550    |
| 1.4753        | 16.48 | 4500 | 1.7181          | 0.5305 | 0.2179 | 0.2539 | 0.2542    |
| 1.4792        | 18.3  | 5000 | 1.7152          | 0.5375 | 0.2258 | 0.2586 | 0.2588    |
| 1.4206        | 20.13 | 5500 | 1.7142          | 0.5366 | 0.2216 | 0.2555 | 0.2556    |
| 1.4273        | 21.96 | 6000 | 1.7128          | 0.5364 | 0.2232 | 0.2573 | 0.2573    |
| 1.4078        | 23.78 | 6500 | 1.7114          | 0.5344 | 0.2200 | 0.2562 | 0.2563    |
| 1.355         | 25.61 | 7000 | 1.7153          | 0.5354 | 0.2212 | 0.2564 | 0.2564    |
| 1.409         | 27.44 | 7500 | 1.7119          | 0.5363 | 0.2217 | 0.2568 | 0.2570    |
| 1.3817        | 29.26 | 8000 | 1.7166          | 0.5369 | 0.2229 | 0.2582 | 0.2582    |


### Framework versions

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