k3mbed: Indonesian OSH Sentence Embedding Model
This is a sentence-transformers model finetuned from indobenchmark/indobert-base-p1. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. This model was trained from data scraped from open access Indonesian occupational safety and health (OSH or K3 in Indonesian) materials available on the internet.
Model Details
Model Description
- Model Type: Sentence Transformer
- Base model: indobenchmark/indobert-base-p1
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 768 dimensions
- Language: Indonesian
Model Sources
- Documentation: Sentence Transformers Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Sentence Transformers on Hugging Face
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
Usage
Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
pip install -U sentence-transformers
Then you can load this model and run inference.
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("wira-pratama/k3mbed-tsdae-v1")
Training Details
Training Dataset
Private Dataset from Public Access Data
- Size: 19,977 training samples
- Columns: sentence_0 and sentence_1
- Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 type string string details min: 4 tokensmean: 11.79 tokensmax: 41 tokens min: 15 tokensmean: 26.36 tokensmax: 107 tokens - Loss: DenoisingAutoEncoderLoss
Non-Default Training Hyperparameters
num_train_epochs: 10multi_dataset_batch_sampler: round_robin
Citation
BibTeX
Sentence Transformers
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
DenoisingAutoEncoderLoss
@inproceedings{wang-2021-TSDAE,
title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning",
author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
pages = "671--688",
url = "https://arxiv.org/abs/2104.06979",
}
Model Card Authors
This model and model card was created and maintained by the following contributors:
- Adi Wira Pratama – Primary author, responsible for data curation, model training, evaluation, and documentation.