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deberta-v3-xsmall-indonesia-squadv2
This model is a fine-tuned version of microsoft/deberta-v3-xsmall on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.4182
Model description
Deberta-V3-Xsmall from Microsft with model parameter: Backbone Parameters 22M, 384 Hidden Size, 12 Layers Based Deberta-V3
Intended uses & limitations
More information needed
Training and evaluation data
Training and evaluation data using Indonesia SQuAD V2, source from https://github.com/Wikidepia/SQuAD-id
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
1.6078 | 1.0 | 13505 | 1.5331 |
1.4216 | 2.0 | 27010 | 1.4344 |
1.2017 | 3.0 | 40515 | 1.4182 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
Evaluation Results
{'exact': 55.34646711872568,
'f1': 67.22757187614371,
'total': 24923,
'HasAns_exact': 55.34646711872568,
'HasAns_f1': 67.22757187614371,
'HasAns_total': 24923,
'best_exact': 55.34646711872568,
'best_exact_thresh': 0.0,
'best_f1': 67.22757187614371,
'best_f1_thresh': 0.0}
Simple Usage
from transformers import pipeline
qa_pipeline = pipeline(
"question-answering",
model="asaduas/deberta-v3-xsmall-indonesia-squadv2",
tokenizer="asaduas/deberta-v3-xsmall-indonesia-squadv2"
)
qa_pipeline(
{
'context': "Pada tahun 1512 juga Afonso de Albuquerque mengirim Antonio Albreu dan Franscisco Serrao untuk memimpin armadanya mencari jalan ke tempat asal rempah-rempah di Maluku. Sepanjang perjalanan, mereka singgah di Madura, Bali, dan Lombok. Dengan menggunakan nakhoda-nakhoda Jawa, armada itu tiba di Kepulauan Banda, terus menuju Aibku Utara sampai tiba di Ternate.",
'question': "Siapa yang dikirim oleh Afonso de Albuquerque Pada tahun 1512?"
}
)
Output
[
{'score': 0.8919295072555542,
'start': 51,
'end': 88,
'answer': ' Antonio Albreu dan Franscisco Serrao'}
]