---
library_name: transformers
license: apache-2.0
base_model: allenai/led-base-16384
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
- precision
- recall
- f1
model-index:
- name: LED_sum_challenge
  results: []
---

<!-- 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_sum_challenge

This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8042
- Rouge1: 0.2495
- Rouge2: 0.0724
- Rougel: 0.1912
- Rougelsum: 0.192
- Gen Len: 20.5
- Bleu: 0.0232
- Precisions: 0.0926
- Brevity Penalty: 0.6016
- Length Ratio: 0.6631
- Translation Length: 801.0
- Reference Length: 1208.0
- Precision: 0.8797
- Recall: 0.8676
- F1: 0.8736
- Hashcode: roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.0)

## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Bleu   | Precisions | Brevity Penalty | Length Ratio | Translation Length | Reference Length | Precision | Recall | F1     | Hashcode                                                  |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:------:|:----------:|:---------------:|:------------:|:------------------:|:----------------:|:---------:|:------:|:------:|:---------------------------------------------------------:|
| No log        | 1.0   | 7    | 8.1739          | 0.2255 | 0.0527 | 0.1686 | 0.1688    | 21.0    | 0.0157 | 0.069      | 0.6607          | 0.707        | 854.0              | 1208.0           | 0.8668    | 0.8574 | 0.862  | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.0) |
| No log        | 2.0   | 14   | 6.9457          | 0.2251 | 0.0588 | 0.1702 | 0.1685    | 20.7    | 0.0171 | 0.0737     | 0.6408          | 0.6921       | 836.0              | 1208.0           | 0.8737    | 0.8597 | 0.8666 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.0) |
| No log        | 3.0   | 21   | 5.4862          | 0.2391 | 0.0632 | 0.181  | 0.1805    | 20.52   | 0.021  | 0.0825     | 0.6431          | 0.6937       | 838.0              | 1208.0           | 0.8798    | 0.862  | 0.8708 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.0) |
| No log        | 4.0   | 28   | 4.7435          | 0.243  | 0.0758 | 0.1901 | 0.1892    | 20.72   | 0.0266 | 0.0886     | 0.6095          | 0.6689       | 808.0              | 1208.0           | 0.8775    | 0.8662 | 0.8717 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.0) |
| No log        | 5.0   | 35   | 4.3805          | 0.2557 | 0.0788 | 0.1924 | 0.1921    | 20.48   | 0.0248 | 0.1003     | 0.5857          | 0.6515       | 787.0              | 1208.0           | 0.8811    | 0.8686 | 0.8747 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.0) |
| No log        | 6.0   | 42   | 4.1441          | 0.2485 | 0.0701 | 0.1886 | 0.1894    | 20.52   | 0.0209 | 0.0929     | 0.5982          | 0.6606       | 798.0              | 1208.0           | 0.8816    | 0.868  | 0.8747 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.0) |
| No log        | 7.0   | 49   | 3.9952          | 0.2574 | 0.0713 | 0.1994 | 0.1997    | 20.54   | 0.0213 | 0.0954     | 0.6073          | 0.6672       | 806.0              | 1208.0           | 0.8811    | 0.8689 | 0.8749 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.0) |
| No log        | 8.0   | 56   | 3.8994          | 0.2524 | 0.067  | 0.192  | 0.192     | 20.58   | 0.0203 | 0.0908     | 0.614           | 0.6722       | 812.0              | 1208.0           | 0.8782    | 0.8675 | 0.8727 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.0) |
| No log        | 9.0   | 63   | 3.8355          | 0.2512 | 0.0676 | 0.1917 | 0.1925    | 20.54   | 0.0201 | 0.0901     | 0.6062          | 0.6664       | 805.0              | 1208.0           | 0.8793    | 0.8681 | 0.8736 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.0) |
| No log        | 10.0  | 70   | 3.8042          | 0.2495 | 0.0724 | 0.1912 | 0.192     | 20.5    | 0.0232 | 0.0926     | 0.6016          | 0.6631       | 801.0              | 1208.0           | 0.8797    | 0.8676 | 0.8736 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.0) |


### Framework versions

- Transformers 4.53.0
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1