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BUOD_distilBART_TM – AI Model by ateneoscsl | AlphaNeural AI
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BUOD_distilBART_TM
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generated_from_trainer
tl
en
ateneoscsl/BUOD_articlescraper
cnn_dailymail
xsum
apache-2.0
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📋 BUOD: distilBART Transformer Model
Model:distilBART
Authors:
James Esguerra
,
Julia Avila
,
Hazielle Bugayong
This model is a fine-tuned version of
sshleifer/distilbart-cnn-12-6
on the KAMI-3000 dataset, for the task of Filipino Text Summarization.
It achieves the following results on the evaluation set:
Loss: 1.8049
Rouge1: 50.5143
Rouge2: 23.2481
Rougel: 34.135
Rougelsum: 46.4261
🔧 Finetuning/ Training procedure
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 5e-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: 2
Training results
Training Loss
Epoch
Step
Validation Loss
Rouge1
Rouge2
Rougel
Rougelsum
2.1377
1.0
586
1.8792
49.8737
22.7881
33.6698
45.8037
1.5731
2.0
1172
1.8049
50.5143
23.2481
34.135
46.4261
Framework versions
Transformers 4.25.1
Pytorch 1.12.1+cu113
Datasets 2.7.1
Tokenizers 0.13.2
