---
license: apache-2.0
base_model: allenai/led-base-16384
tags:
- generated_from_trainer
datasets:
- wcep-10
metrics:
- rouge
model-index:
- name: thesis-led-finetuned-on-wcep
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: wcep-10
      type: wcep-10
      config: roberta
      split: validation
      args: roberta
    metrics:
    - name: Rouge1
      type: rouge
      value: 43.4358
---

<!-- 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. -->

# thesis-led-finetuned-on-wcep

This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the wcep-10 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6816
- Rouge1: 43.4358
- Rouge2: 21.8159
- Rougel: 35.0411
- Rougelsum: 36.1007
- Gen Len: 27.2843

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.6843        | 1.0   | 2040 | 1.6753          | 42.8519 | 21.8933 | 35.0226 | 35.9911   | 25.6647 |
| 1.4083        | 2.0   | 4080 | 1.6672          | 43.5166 | 22.0845 | 35.283  | 36.4006   | 26.4098 |
| 1.1981        | 3.0   | 6120 | 1.6816          | 43.4358 | 21.8159 | 35.0411 | 36.1007   | 27.2843 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2