metadata
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: []
LED_sum_challenge
This model is a fine-tuned version of 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