Folma/IndoBERT-Retail-Search-Model

SentenceTransformer based on indobenchmark/indobert-base-p2

This is a sentence-transformers model finetuned from indobenchmark/indobert-base-p2. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: indobenchmark/indobert-base-p2
• Maximum Sequence Length: 128 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Supported Modality: Text

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', '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("Folma/IndoBERT-Retail-Search-Model")
# Run inference
sentences = [
    'KATALOG PRODUK ASEPSO SABUN BADAN BATANG',
    'ASEPSO BAR SOAP FRESH 80 GR',
    'JOHNSONS BABY OIL 125 ML',
]
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,  0.8572, -0.1430],
#         [ 0.8572,  1.0000, -0.1383],
#         [-0.1430, -0.1383,  1.0000]])

Evaluation

Metrics

Information Retrieval

• Dataset: retail-production-eval
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.3367
cosine_accuracy@3	0.5963
cosine_accuracy@5	0.7027
cosine_accuracy@10	0.8207
cosine_precision@1	0.3367
cosine_precision@3	0.1988
cosine_precision@5	0.1405
cosine_precision@10	0.0821
cosine_recall@1	0.3367
cosine_recall@3	0.5963
cosine_recall@5	0.7027
cosine_recall@10	0.8207
cosine_ndcg@10	0.5704
cosine_mrr@10	0.4912
cosine_map@100	0.4988

Training Details

Training Dataset

Unnamed Dataset

• Size: 54,541 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 4 tokens mean: 8.5 tokens max: 20 tokens	min: 5 tokens mean: 11.18 tokens max: 21 tokens	min: 4 tokens mean: 11.17 tokens max: 21 tokens

• Samples:

  anchor	positive	negative
  IKAN LAUT MERK SEAFOOD	IKAN FRESH KEMBUNG COMO KG	MABELL SOSIS KEJU 60GR
  KATALOG PRODUK BU SANDRA SAUS SAMBAL	BU SANDRA SAMBAL UDANG 150g	CADBURY LICKABLE DAIRY MILK 20GR
  PILIHAN BUMBU KOREA DARI O FOOD	O'FOOD KIMCHI JJIGAE SAUCE 120g	MAMASUKA RUMPUT LAUT RENDANG 4.5X2

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 64
• num_train_epochs: 50
• learning_rate: 2e-05
• warmup_steps: 0.1
• weight_decay: 0.01
• fp16: True
• eval_strategy: steps
• load_best_model_at_end: True

All Hyperparameters

Click to expand

• per_device_train_batch_size: 64
• num_train_epochs: 50
• max_steps: -1
• learning_rate: 2e-05
• lr_scheduler_type: linear
• lr_scheduler_kwargs: None
• warmup_steps: 0.1
• optim: adamw_torch_fused
• optim_args: None
• weight_decay: 0.01
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• optim_target_modules: None
• gradient_accumulation_steps: 1
• average_tokens_across_devices: True
• max_grad_norm: 1.0
• label_smoothing_factor: 0.0
• bf16: False
• fp16: True
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• use_liger_kernel: False
• liger_kernel_config: None
• use_cache: False
• neftune_noise_alpha: None
• torch_empty_cache_steps: None
• auto_find_batch_size: False
• log_on_each_node: True
• logging_nan_inf_filter: True
• include_num_input_tokens_seen: no
• log_level: passive
• log_level_replica: warning
• disable_tqdm: False
• project: huggingface
• trackio_space_id: trackio
• eval_strategy: steps
• per_device_eval_batch_size: 8
• prediction_loss_only: True
• eval_on_start: False
• eval_do_concat_batches: True
• eval_use_gather_object: False
• eval_accumulation_steps: None
• include_for_metrics: []
• batch_eval_metrics: False
• save_only_model: False
• save_on_each_node: False
• enable_jit_checkpoint: False
• push_to_hub: False
• hub_private_repo: None
• hub_model_id: None
• hub_strategy: every_save
• hub_always_push: False
• hub_revision: None
• load_best_model_at_end: True
• ignore_data_skip: False
• restore_callback_states_from_checkpoint: False
• full_determinism: False
• seed: 42
• data_seed: None
• use_cpu: False
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• parallelism_config: None
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• dataloader_prefetch_factor: None
• remove_unused_columns: True
• label_names: None
• train_sampling_strategy: random
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• ddp_backend: None
• ddp_timeout: 1800
• fsdp: []
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• deepspeed: None
• debug: []
• skip_memory_metrics: True
• do_predict: False
• resume_from_checkpoint: None
• warmup_ratio: None
• local_rank: -1
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	retail-production-eval_cosine_ndcg@10
0.5862	500	1.6535	0.4757
1.1723	1000	0.8146	0.5074
1.7585	1500	0.5929	0.5271
2.3447	2000	0.4561	0.5407
2.9308	2500	0.4064	0.5501
3.5170	3000	0.3404	0.5539
4.1032	3500	0.3302	0.5593
4.6893	4000	0.2986	0.5644
5.2755	4500	0.2868	0.5641
5.8617	5000	0.2767	0.5644
6.4478	5500	0.2532	0.571
7.0340	6000	0.2495	0.5684
7.6202	6500	0.2346	0.5676
8.2063	7000	0.2286	0.5704

• The bold row denotes the saved checkpoint.

Training Time

• Training: 19.3 minutes

Framework Versions

• Python: 3.12.13
• Sentence Transformers: 5.4.0
• Transformers: 5.5.4
• PyTorch: 2.10.0+cu128
• Accelerate: 1.13.0
• Datasets: 4.0.0
• Tokenizers: 0.22.2

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{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}