---
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: LEDv3_ACLsum_all_aspects
  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. -->

# LEDv3_ACLsum_all_aspects

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: 2.2405
- Rouge1: 0.3564
- Rouge2: 0.1444
- Rougel: 0.296
- Rougelsum: 0.2951
- Gen Len: 20.96
- Bleu: 0.0646
- Precisions: 0.1586
- Brevity Penalty: 0.5945
- Length Ratio: 0.6579
- Translation Length: 2369.0
- Reference Length: 3601.0
- Precision: 0.892
- Recall: 0.8776
- F1: 0.8846
- Hashcode: roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1)

## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- 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

### 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   | 19   | 4.0849          | 0.2835 | 0.0826 | 0.2239 | 0.2243    | 20.5333 | 0.0374 | 0.105      | 0.6263          | 0.6812       | 2453.0             | 3601.0           | 0.8837    | 0.8674 | 0.8754 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
| No log        | 2.0   | 38   | 3.1922          | 0.2787 | 0.0853 | 0.2229 | 0.2223    | 20.7667 | 0.0396 | 0.1061     | 0.6161          | 0.6737       | 2426.0             | 3601.0           | 0.8767    | 0.8646 | 0.8705 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
| No log        | 3.0   | 57   | 2.8716          | 0.292  | 0.098  | 0.2328 | 0.2329    | 20.84   | 0.0461 | 0.1181     | 0.6082          | 0.6679       | 2405.0             | 3601.0           | 0.8812    | 0.8688 | 0.8749 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
| No log        | 4.0   | 76   | 2.6529          | 0.3166 | 0.1192 | 0.2573 | 0.2566    | 20.92   | 0.0576 | 0.1366     | 0.6104          | 0.6695       | 2411.0             | 3601.0           | 0.8861    | 0.8725 | 0.8792 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
| No log        | 5.0   | 95   | 2.5101          | 0.3441 | 0.1353 | 0.282  | 0.2813    | 20.94   | 0.0613 | 0.1495     | 0.593           | 0.6568       | 2365.0             | 3601.0           | 0.8895    | 0.8763 | 0.8828 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
| No log        | 6.0   | 114  | 2.3985          | 0.3501 | 0.1415 | 0.2909 | 0.2912    | 20.92   | 0.0616 | 0.1514     | 0.5983          | 0.6606       | 2379.0             | 3601.0           | 0.8913    | 0.8771 | 0.884  | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
| No log        | 7.0   | 133  | 2.3215          | 0.3557 | 0.1398 | 0.295  | 0.2949    | 20.9667 | 0.0608 | 0.1545     | 0.593           | 0.6568       | 2365.0             | 3601.0           | 0.8919    | 0.8775 | 0.8846 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
| No log        | 8.0   | 152  | 2.2783          | 0.3494 | 0.1417 | 0.2922 | 0.2918    | 20.9333 | 0.0637 | 0.1561     | 0.588           | 0.6532       | 2352.0             | 3601.0           | 0.8907    | 0.8769 | 0.8837 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
| No log        | 9.0   | 171  | 2.2467          | 0.3566 | 0.145  | 0.297  | 0.2968    | 20.96   | 0.0649 | 0.1591     | 0.5926          | 0.6565       | 2364.0             | 3601.0           | 0.8921    | 0.8775 | 0.8846 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
| No log        | 10.0  | 190  | 2.2405          | 0.3564 | 0.1444 | 0.296  | 0.2951    | 20.96   | 0.0646 | 0.1586     | 0.5945          | 0.6579       | 2369.0             | 3601.0           | 0.892     | 0.8776 | 0.8846 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |


### Framework versions

- Transformers 4.53.1
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1