---
license: apache-2.0
base_model: allenai/led-base-16384
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: LED_billsum_model
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: billsum
      type: billsum
      config: default
      split: ca_test
      args: default
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.1447
---

<!-- 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_billsum_model

This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6576
- Rouge1: 0.1447
- Rouge2: 0.0854
- Rougel: 0.1292
- Rougelsum: 0.1339
- Gen Len: 20.0

## 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: 2e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.4849        | 1.0   | 330  | 1.6511          | 0.1463 | 0.0827 | 0.1276 | 0.1337    | 20.0    |
| 1.3361        | 2.0   | 660  | 1.6056          | 0.148  | 0.0799 | 0.1268 | 0.1336    | 20.0    |
| 1.1727        | 3.0   | 990  | 1.5833          | 0.1459 | 0.0827 | 0.1289 | 0.1341    | 20.0    |
| 1.0601        | 4.0   | 1320 | 1.5987          | 0.1462 | 0.0859 | 0.1299 | 0.1344    | 20.0    |
| 0.9789        | 5.0   | 1650 | 1.6030          | 0.1414 | 0.0794 | 0.125  | 0.1302    | 20.0    |
| 0.8724        | 6.0   | 1980 | 1.6060          | 0.1476 | 0.0868 | 0.1298 | 0.1356    | 20.0    |
| 0.7994        | 7.0   | 2310 | 1.6295          | 0.1348 | 0.0758 | 0.1198 | 0.1253    | 20.0    |
| 0.7762        | 8.0   | 2640 | 1.6317          | 0.1422 | 0.0831 | 0.1261 | 0.1312    | 20.0    |
| 0.7087        | 9.0   | 2970 | 1.6501          | 0.1421 | 0.0825 | 0.1264 | 0.1311    | 20.0    |
| 0.7014        | 10.0  | 3300 | 1.6576          | 0.1447 | 0.0854 | 0.1292 | 0.1339    | 20.0    |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1