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nomic-embed-indonesian

Sentence Similarity
sentence-transformers
Safetensors
nomic_bert
feature-extraction
dense
Generated from Trainer
dataset_size:6294
loss:MultipleNegativesRankingLoss
custom_code
Eval Results
text-embeddings-inference
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nomic-embed-indonesian

This is a sentence-transformers model finetuned from nomic-ai/nomic-embed-text-v1.5 specifically for Indonesian language text embedding tasks. It maps Indonesian 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.

🚀 Quick Start 

from sentence_transformers import SentenceTransformer

# Load the model (requires trust_remote_code=True)
model = SentenceTransformer("asmud/nomic-embed-indonesian", trust_remote_code=True)

# Indonesian text examples
texts = [
    "search_query: Apa itu kecerdasan buatan?",
    "search_document: Kecerdasan buatan adalah teknologi yang memungkinkan mesin belajar",
    "classification: Produk ini sangat berkualitas (sentimen: positif)"
]

# Generate embeddings
embeddings = model.encode(texts)
print(f"Embedding shape: {embeddings.shape}")  # (3, 768)

🇮🇩 Specialized for Indonesian Language

This model is optimized for Indonesian text understanding across multiple domains including:

  • Technology (Teknologi) - AI, gadgets, digital innovation
  • Politics (Politik) - Government, elections, public policy
  • Law (Hukum) - Legal affairs, crime, justice
  • Economy (Ekonomi) - Business, finance, trade
  • Education (Pendidikan) - Academic, learning, research
  • Health (Kesehatan) - Medical, wellness, healthcare
  • Sports (Olahraga) - Athletics, competitions, fitness
  • Culture (Budaya) - Literature, arts, traditions
  • And more...

Model Details

Model Description

  • Model Type: Sentence Transformer
  • Base model: nomic-ai/nomic-embed-text-v1.5
  • Maximum Sequence Length: 8192 tokens
  • Output Dimensionality: 768 dimensions
  • Similarity Function: Cosine Similarity

Model Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False, 'architecture': 'NomicBertModel'})
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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

⚠️ Important: This model requires trust_remote_code=True due to custom model architecture.

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("asmud/nomic-embed-indonesian", trust_remote_code=True)
# Run inference with Indonesian text
sentences = [
    'search_query: Apa itu kecerdasan buatan?',
    'search_document: Kecerdasan buatan adalah teknologi yang memungkinkan mesin belajar dari data',
    'classification: Produk ini sangat berkualitas dan sesuai harapan (sentimen: positif)',
    'clustering: makanan tradisional Indonesia seperti rendang dan gudeg',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7154, 0.7378],
#         [0.7154, 1.0000, 0.6583],
#         [0.7378, 0.6583, 1.0000]])

Evaluation

Metrics

Semantic Similarity

Metric Value
pearson_cosine 0.4358
spearman_cosine 0.2857

Training Details

Training Dataset

Unnamed Dataset

  • Size: 6,294 training samples
  • Columns: sentence_0, sentence_1, and label
  • Approximate statistics based on the first 1000 samples:
    sentence_0 sentence_1 label
    type string string float
    details
    • min: 8 tokens
    • mean: 20.45 tokens
    • max: 181 tokens
    • min: 7 tokens
    • mean: 117.93 tokens
    • max: 508 tokens
    • min: 0.0
    • mean: 0.51
    • max: 1.0
  • Samples:
    sentence_0 sentence_1 label
    clustering: artikel berita Indonesia clustering: Paris Saint - Germain gagal mempertahankan status tak terkalahkan di Ligue 1 Prancis , setelah dipaksa menelan kekalahan perdana musim ini kala menyambangi Strasbourg . Tanda - tanda kurang maksimalnya performa klub ibukota Prancis ini sudah terlihat di awal pertandingan . Lini belakang gagal mengantisipasi skema tendangan bebas Strasbourg sehingga umpan Dimitri Lienard diteruskan dengan mudah oleh Nuno Da Costa pada menit ke - 13 untuk mencetak gol pembuka . Skuat asuhan Unai Emery langsung bermain agresif untuk mengejar ketertinggalan , mengandalkan trio Neymar , Kylian Mbappe dan Angel Di Maria . Nama terakhir mendapat kesempatan pada menit ke - 39 usai menerima umpan terobosan dari Neymar , tetapi sayang sepakannya gagal menemui sasaran meski sudah tidak dapat diantisipasi kiper . Mbappe akhirnya yang sukses mencatatkan namanya di papan skor . Mantan pemain Monaco itu menyambar umpan tarik Rabiot di dalam kotak penalti pada menit ke - 42 untuk membuat skor sama kuat . B... 1.0
    search_query: KPK resmi menetapkan Ketua DPR Setya Novanto sebag search_document: KPK resmi menetapkan Ketua DPR Setya Novanto sebagai tersangka kasus korupsi pengadaan proyek e - KTP . Penetapan status tersangka yang kedua kalinya ini disampaikan Wakil Ketua KPK Saut Situmorang . Novanto dijerat dengan Pasal 2 ayat 1 subsider Pasal 3 Undang-Undang Nomor 31 tahun 1999 sebagaimana diubah dengan Undang-Undang Nomor 20 tahun 2001 tentang Pemberantasan Korupsi juncto Pasal 55 ayat 1 ke - 1 KUHP . 1.0
    search_query: Google memperkenalkan laptop chromebook kelas atas classification: ga da wifi d lantai 2,kamar mandi ga da gantungan handuk or baju,over all bagus,n recomended (sentimen: positif) 0.0
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

  • per_device_train_batch_size: 1
  • per_device_eval_batch_size: 1
  • num_train_epochs: 1
  • multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: no
  • prediction_loss_only: True
  • per_device_train_batch_size: 1
  • per_device_eval_batch_size: 1
  • per_gpu_train_batch_size: None
  • per_gpu_eval_batch_size: None
  • gradient_accumulation_steps: 1
  • eval_accumulation_steps: None
  • torch_empty_cache_steps: None
  • learning_rate: 5e-05
  • weight_decay: 0.0
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1
  • num_train_epochs: 1
  • max_steps: -1
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.0
  • warmup_steps: 0
  • log_level: passive
  • log_level_replica: warning
  • log_on_each_node: True
  • logging_nan_inf_filter: True
  • save_safetensors: True
  • save_on_each_node: False
  • save_only_model: False
  • restore_callback_states_from_checkpoint: False
  • no_cuda: False
  • use_cpu: False
  • use_mps_device: False
  • seed: 42
  • data_seed: None
  • jit_mode_eval: False
  • use_ipex: False
  • bf16: False
  • fp16: False
  • fp16_opt_level: O1
  • half_precision_backend: auto
  • bf16_full_eval: False
  • fp16_full_eval: False
  • tf32: None
  • local_rank: 0
  • ddp_backend: None
  • tpu_num_cores: None
  • tpu_metrics_debug: False
  • debug: []
  • dataloader_drop_last: False
  • dataloader_num_workers: 0
  • dataloader_prefetch_factor: None
  • past_index: -1
  • disable_tqdm: False
  • remove_unused_columns: True
  • label_names: None
  • load_best_model_at_end: False
  • ignore_data_skip: False
  • fsdp: []
  • fsdp_min_num_params: 0
  • fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
  • fsdp_transformer_layer_cls_to_wrap: None
  • accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
  • deepspeed: None
  • label_smoothing_factor: 0.0
  • optim: adamw_torch
  • optim_args: None
  • adafactor: False
  • group_by_length: False
  • length_column_name: length
  • ddp_find_unused_parameters: None
  • ddp_bucket_cap_mb: None
  • ddp_broadcast_buffers: False
  • dataloader_pin_memory: True
  • dataloader_persistent_workers: False
  • skip_memory_metrics: True
  • use_legacy_prediction_loop: False
  • push_to_hub: False
  • resume_from_checkpoint: None
  • hub_model_id: None
  • hub_strategy: every_save
  • hub_private_repo: None
  • hub_always_push: False
  • hub_revision: None
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • include_inputs_for_metrics: False
  • include_for_metrics: []
  • eval_do_concat_batches: True
  • fp16_backend: auto
  • push_to_hub_model_id: None
  • push_to_hub_organization: None
  • mp_parameters:
  • auto_find_batch_size: False
  • full_determinism: False
  • torchdynamo: None
  • ray_scope: last
  • ddp_timeout: 1800
  • torch_compile: False
  • torch_compile_backend: None
  • torch_compile_mode: None
  • include_tokens_per_second: False
  • include_num_input_tokens_seen: False
  • neftune_noise_alpha: None
  • optim_target_modules: None
  • batch_eval_metrics: False
  • eval_on_start: False
  • use_liger_kernel: False
  • liger_kernel_config: None
  • eval_use_gather_object: False
  • average_tokens_across_devices: False
  • prompts: None
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: round_robin
  • router_mapping: {}
  • learning_rate_mapping: {}

Training Logs

Epoch Step Training Loss indonesian-diversity-eval_spearman_cosine
0.0794 500 0.0 -
0.1589 1000 0.0 -
0.2383 1500 0.0 -
0.3178 2000 0.0 -
0.3972 2500 0.0 -
0.4766 3000 0.0 -
0.5561 3500 0.0 -
0.6355 4000 0.0 -
0.7150 4500 0.0 -
0.7944 5000 0.0 -
0.8738 5500 0.0 -
0.9533 6000 0.0 -
1.0 6294 - 0.2857

Framework Versions

  • Python: 3.11.13
  • Sentence Transformers: 5.0.0
  • Transformers: 4.54.1
  • PyTorch: 2.7.1
  • Accelerate: 1.9.0
  • Datasets: 4.0.0
  • Tokenizers: 0.21.4

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",
}

MultipleNegativesRankingLoss 

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
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