---
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_outcome
  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_outcome

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: 3.4730
- Rouge1: 0.3601
- Rouge2: 0.1515
- Rougel: 0.3017
- Rougelsum: 0.301
- Gen Len: 20.36
- Bleu: 0.0595
- Precisions: 0.1544
- Brevity Penalty: 0.6108
- Length Ratio: 0.6698
- Translation Length: 785.0
- Reference Length: 1172.0
- Precision: 0.8997
- Recall: 0.8766
- F1: 0.8879
- 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    | 7.7628          | 0.2668 | 0.0617 | 0.2177 | 0.2179    | 21.0    | 0.0211 | 0.0856     | 0.6935          | 0.7321       | 858.0              | 1172.0           | 0.8742    | 0.86   | 0.8669 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.0) |
| No log        | 2.0   | 14   | 6.5648          | 0.3427 | 0.124  | 0.2806 | 0.2804    | 20.16   | 0.0514 | 0.1396     | 0.6085          | 0.6681       | 783.0              | 1172.0           | 0.8991    | 0.8717 | 0.8851 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.0) |
| No log        | 3.0   | 21   | 5.1851          | 0.3468 | 0.1383 | 0.282  | 0.2807    | 19.7    | 0.0722 | 0.1711     | 0.578           | 0.6459       | 757.0              | 1172.0           | 0.9029    | 0.8772 | 0.8898 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.0) |
| No log        | 4.0   | 28   | 4.4398          | 0.3475 | 0.1299 | 0.2825 | 0.2821    | 20.18   | 0.0455 | 0.1417     | 0.598           | 0.6604       | 774.0              | 1172.0           | 0.8979    | 0.8766 | 0.887  | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.0) |
| No log        | 5.0   | 35   | 4.0655          | 0.3506 | 0.1412 | 0.2913 | 0.2901    | 19.94   | 0.054  | 0.1556     | 0.5685          | 0.6391       | 749.0              | 1172.0           | 0.8987    | 0.8772 | 0.8877 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.0) |
| No log        | 6.0   | 42   | 3.8299          | 0.356  | 0.148  | 0.295  | 0.294     | 20.38   | 0.0616 | 0.1566     | 0.6073          | 0.6672       | 782.0              | 1172.0           | 0.9002    | 0.8781 | 0.8889 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.0) |
| No log        | 7.0   | 49   | 3.6727          | 0.3645 | 0.144  | 0.2966 | 0.296     | 20.38   | 0.0637 | 0.1593     | 0.6108          | 0.6698       | 785.0              | 1172.0           | 0.8987    | 0.8774 | 0.8878 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.0) |
| No log        | 8.0   | 56   | 3.5737          | 0.3586 | 0.146  | 0.2948 | 0.2941    | 20.44   | 0.0632 | 0.1563     | 0.6201          | 0.6766       | 793.0              | 1172.0           | 0.8965    | 0.8762 | 0.8862 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.0) |
| No log        | 9.0   | 63   | 3.5072          | 0.3568 | 0.1486 | 0.2976 | 0.2963    | 20.36   | 0.0598 | 0.1536     | 0.6189          | 0.6758       | 792.0              | 1172.0           | 0.8986    | 0.8769 | 0.8875 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.0) |
| No log        | 10.0  | 70   | 3.4730          | 0.3601 | 0.1515 | 0.3017 | 0.301     | 20.36   | 0.0595 | 0.1544     | 0.6108          | 0.6698       | 785.0              | 1172.0           | 0.8997    | 0.8766 | 0.8879 | 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