IndoNanoT5-base

IndoNanoT5 Base

IndoNanoT5 Base is an Indonesian sequence-to-sequence language model based on the T5 architecture. We conducted pre-training on an open-source Indonesian corpus of uonlp/CulturaX. On a held-out subset of the corpus, our model achieved an evaluation loss of 2.082 or a perplexity of about 8.02.

This model was trained using the nanoT5 PyTorch framework. All training was done on an NVIDIA H100 GPU. LazarusNLP/IndoNanoT5-base is released under Apache 2.0 license.

Model Detail

• Developed by: LazarusNLP
• Model type: Encoder-decoder T5 transformer language model
• Language(s): Indonesian
• License: Apache 2.0
• Contact: Wilson Wongso

Use in 🤗Transformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_checkpoint = "LazarusNLP/IndoNanoT5-base"

tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)

Training Datasets

Around 4B tokens from the following corpora were used during pre-training.

• Cleaned, Enormous, and Public: The Multilingual Fuel to Democratize Large Language Models for 167 Languages

Training Hyperparameters

The following hyperparameters were used during training:

• total_steps: 65536
• input_length: 512
• batch_size: 128
• grad_acc: 1
• base_lr: 5e-3
• optimizer: AdamWScaled with betas=(0.9,0.999) and epsilon=1e-08
• weight_decay: 0.0
• lr_scheduler: cosine
• warmup_steps: 10000
• final_cosine: 1e-5
• grad_clip: 1.0
• precision: bf16

Acknowledgements

We would like to acknowledge nanoT5 for inspiring this project.

Credits

BhinnekaLM is developed with love by:

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