Distilbert Base Uncased Indonesia Squadv2

🚀 distilbert-base-uncased-indonesia-squadv2

This model is a fine - tuned version of distilbert-base-uncased on an unknown dataset, aiming to solve question - answering tasks.

🚀 Quick Start

This model is a fine - tuned version of distilbert-base-uncased on an unknown dataset. It achieves a loss of 1.9144 on the evaluation set.

✨ Features

This model is fine - tuned for question - answering tasks. You can use it to answer various types of questions given a context.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import pipeline
qa_pipeline = pipeline(
    "question - answering",
    model="asaduas/distilbert-base-uncased-indonesia-squadv2",
    tokenizer="asaduas/distilbert-base-uncased-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'}
]

📚 Documentation

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: 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

Framework versions

• Transformers 4.25.1
• Pytorch 1.12.1+cu113
• Datasets 2.7.1
• Tokenizers 0.13.2

Evaluations result

{'exact': 42.29721064443732,
 'f1': 54.120071422699546,
 'total': 24952,
 'HasAns_exact': 42.29721064443732,
 'HasAns_f1': 54.120071422699546,
 'HasAns_total': 24952,
 'best_exact': 42.29721064443732,
 'best_exact_thresh': 0.0,
 'best_f1': 54.120071422699546,
 'best_f1_thresh': 0.0}

📄 License

This model is licensed under the Apache - 2.0 license.