---
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: 0.5444603858958118
---

<!-- 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: 2.1142
- Rouge1: 0.5445
- Rouge2: 0.2225
- Rougel: 0.2578
- Rougelsum: 0.2579

## 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: 100

### 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    |
| 1.3072        | 31.13 | 8500  | 1.7302          | 0.5379 | 0.2249 | 0.2604 | 0.2603    |
| 1.3172        | 32.96 | 9000  | 1.7121          | 0.5377 | 0.2236 | 0.2588 | 0.2587    |
| 1.277         | 34.78 | 9500  | 1.7255          | 0.5368 | 0.2221 | 0.2584 | 0.2583    |
| 1.1849        | 36.61 | 10000 | 1.7438          | 0.5382 | 0.2244 | 0.2611 | 0.2612    |
| 1.1565        | 38.44 | 10500 | 1.7540          | 0.5414 | 0.2258 | 0.2612 | 0.2612    |
| 1.1415        | 40.26 | 11000 | 1.7707          | 0.5401 | 0.2251 | 0.2618 | 0.2618    |
| 1.085         | 42.09 | 11500 | 1.7791          | 0.5401 | 0.2235 | 0.2595 | 0.2595    |
| 1.088         | 43.92 | 12000 | 1.7869          | 0.5422 | 0.2265 | 0.2616 | 0.2615    |
| 1.0678        | 45.74 | 12500 | 1.8058          | 0.5420 | 0.2253 | 0.2607 | 0.2607    |
| 1.0815        | 47.57 | 13000 | 1.8186          | 0.5405 | 0.2248 | 0.2615 | 0.2615    |
| 1.0456        | 49.4  | 13500 | 1.8346          | 0.5430 | 0.2262 | 0.2619 | 0.2618    |
| 0.9553        | 51.22 | 14000 | 1.8449          | 0.5387 | 0.2239 | 0.2614 | 0.2613    |
| 0.958         | 53.05 | 14500 | 1.8716          | 0.5438 | 0.2274 | 0.2618 | 0.2618    |
| 0.9213        | 54.88 | 15000 | 1.8780          | 0.5438 | 0.2249 | 0.2612 | 0.2612    |
| 0.876         | 56.77 | 15500 | 1.8904          | 0.5439 | 0.2253 | 0.2621 | 0.2621    |
| 0.8967        | 58.6  | 16000 | 1.9085          | 0.5439 | 0.2264 | 0.2634 | 0.2633    |
| 0.9138        | 60.43 | 16500 | 1.9089          | 0.5428 | 0.2242 | 0.2597 | 0.2597    |
| 0.848         | 62.25 | 17000 | 1.9153          | 0.5441 | 0.2242 | 0.2600 | 0.2599    |
| 0.7804        | 64.08 | 17500 | 1.9311          | 0.5422 | 0.2241 | 0.2603 | 0.2604    |
| 0.8326        | 65.91 | 18000 | 1.9391          | 0.5446 | 0.2242 | 0.2604 | 0.2602    |
| 0.8164        | 67.73 | 18500 | 1.9607          | 0.5430 | 0.2245 | 0.2607 | 0.2607    |
| 0.8129        | 69.56 | 19000 | 1.9731          | 0.5456 | 0.2277 | 0.2633 | 0.2633    |
| 0.8049        | 71.39 | 19500 | 1.9804          | 0.5433 | 0.2248 | 0.2618 | 0.2619    |
| 0.7605        | 73.21 | 20000 | 2.0060          | 0.5449 | 0.2256 | 0.2607 | 0.2606    |
| 0.7595        | 75.04 | 20500 | 2.0085          | 0.5425 | 0.2227 | 0.2590 | 0.2590    |
| 0.7837        | 76.87 | 21000 | 2.0073          | 0.5441 | 0.2243 | 0.2608 | 0.2609    |
| 0.7458        | 78.69 | 21500 | 2.0210          | 0.5447 | 0.2260 | 0.2619 | 0.2621    |
| 0.7235        | 80.52 | 22000 | 2.0273          | 0.5445 | 0.2253 | 0.2610 | 0.2611    |
| 0.7405        | 82.35 | 22500 | 2.0405          | 0.5438 | 0.2243 | 0.2600 | 0.2599    |
| 0.7323        | 84.17 | 23000 | 2.0385          | 0.5466 | 0.2256 | 0.2607 | 0.2608    |
| 0.7333        | 86.0  | 23500 | 2.0386          | 0.5447 | 0.2248 | 0.2608 | 0.2609    |
| 0.7067        | 87.83 | 24000 | 2.0582          | 0.5449 | 0.2243 | 0.2601 | 0.2600    |
| 0.7073        | 89.65 | 24500 | 2.0615          | 0.5455 | 0.2253 | 0.2604 | 0.2603    |
| 0.6903        | 91.48 | 25000 | 2.0657          | 0.5482 | 0.2273 | 0.2627 | 0.2626    |
| 0.7203        | 93.31 | 25500 | 2.0574          | 0.5452 | 0.2241 | 0.2596 | 0.2597    |
| 0.6765        | 95.13 | 26000 | 2.0692          | 0.5437 | 0.2249 | 0.2608 | 0.2608    |
| 0.6959        | 96.96 | 26500 | 2.0696          | 0.5442 | 0.2246 | 0.2614 | 0.2614    |
| 0.6918        | 98.79 | 27000 | 2.0701          | 0.5444 | 0.2252 | 0.2615 | 0.2615    |


### Framework versions

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