indobart-base

indobart-small

This model is a fine-tuned version of bart-large-cnn on Liputan6 dataset. See demo model here notebook.

Training procedure

Training hyperparameters

• learning_rate: 0.0001
• 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: 1

Training results

Framework versions

• Transformers 4.40.0
• Pytorch 2.2.1+cu121
• Datasets 2.19.0
• Tokenizers 0.19.1

Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Load model and tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained("gaduhhartawan/indobart-base")
tokenizer = AutoTokenizer.from_pretrained("gaduhhartawan/indobart-base")

# Input article for summarization
ARTICLE_TO_SUMMARIZE = "lorem ipsum..."

# Generate summary
input_ids = tokenizer.encode(ARTICLE_TO_SUMMARIZE, return_tensors='pt')
summary_ids = model.generate(input_ids,
            min_length=30,
            max_length=150,
            num_beams=2,
            repetition_penalty=2.0,
            length_penalty=0.8,
            early_stopping=True,
            no_repeat_ngram_size=2,
            use_cache=True,
            do_sample=True,
            temperature=0.7,
            top_k=50,
            top_p=0.95)

# Decode the summary
summary_text = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print("Summary: ", summary_text)