Pustekhan-ITB/LazarusNLP-all-indo-e5-small-v4-exp

SentenceTransformer based on LazarusNLP/all-indo-e5-small-v4

This is a sentence-transformers model finetuned from LazarusNLP/all-indo-e5-small-v4 on the stsb-indo-edu dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: LazarusNLP/all-indo-e5-small-v4
• Maximum Sequence Length: 128 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • stsb-indo-edu
• Language: id

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': 128, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Pustekhan-ITB/LazarusNLP-all-indo-e5-small-v4-exp")
# Run inference
sentences = [
    'Jawaban singkatnya adalah: kita terbuat dari "materi" yang disumbangkan oleh banyak bintang.',
    'Sangat tidak mungkin bahwa kita terbuat dari benda-benda yang hanya terbuat dari satu bintang.',
    'Sebuah band sedang bermain di atas panggung.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Semantic Similarity

• Datasets: stsb-indo-edu-dev and stsb-indo-edu-test
• Evaluated with EmbeddingSimilarityEvaluator

Metric	stsb-indo-edu-dev	stsb-indo-edu-test
pearson_cosine	0.8533	0.8301
spearman_cosine	0.8614	0.83

Training Details

Training Dataset

stsb-indo-edu

• Dataset: stsb-indo-edu at f84d4d6
• Size: 6,198 training samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 6 tokens mean: 10.95 tokens max: 28 tokens	min: 6 tokens mean: 10.81 tokens max: 30 tokens	min: 0.0 mean: 0.46 max: 1.0

• Samples:

  sentence1	sentence2	score
  Pelajaran menari daerah membantu siswa SD melestarikan kebudayaan lokal	Tarian ini sering dipentaskan saat perayaan hari besar	0.76
  Sebelum ujian sekolah, guru memberikan bimbingan belajar tambahan secara gratis	Upaya ini agar seluruh siswa siap menghadapi ujian	0.85
  Beberapa SD terletak di daerah pegunungan, sehingga siswa harus berjalan kaki cukup jauh	Ini melatih kemandirian dan fisik yang kuat	0.63

• Loss: CoSENTLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  

Evaluation Dataset

stsb-indo-edu

• Dataset: stsb-indo-edu at f84d4d6
• Size: 1,536 evaluation samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 5 tokens mean: 15.96 tokens max: 44 tokens	min: 6 tokens mean: 15.97 tokens max: 47 tokens	min: 0.0 mean: 0.42 max: 1.0

• Samples:

  sentence1	sentence2	score
  Seorang pria dengan topi keras sedang menari.	Seorang pria yang mengenakan topi keras sedang menari.	1.0
  Seorang anak kecil sedang menunggang kuda.	Seorang anak sedang menunggang kuda.	0.95
  Seorang pria sedang memberi makan seekor tikus kepada seekor ular.	Pria itu sedang memberi makan seekor tikus kepada ular.	1.0

• Loss: CoSENTLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• warmup_ratio: 0.1
• fp16: True
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• 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: 1.0
• num_train_epochs: 3
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• 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: True
• 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
• 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
• dispatch_batches: None
• split_batches: 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
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	stsb-indo-edu-dev_spearman_cosine	stsb-indo-edu-test_spearman_cosine
-1	-1	-	-	0.7948	-
0.2577	100	6.4446	4.2454	0.8387	-
0.5155	200	4.3775	4.2339	0.8440	-
0.7732	300	4.3249	4.1854	0.8528	-
1.0309	400	4.2454	4.2551	0.8580	-
1.2887	500	4.1675	4.2855	0.8595	-
1.5464	600	4.1579	4.3286	0.8603	-
1.8041	700	4.1222	4.2830	0.8545	-
2.0619	800	3.9992	4.5759	0.8530	-
2.3196	900	3.9653	4.5211	0.8535	-
2.5773	1000	3.9401	4.5906	0.8585	-
2.8351	1100	3.968	4.5313	0.8614	-
-1	-1	-	-	-	0.8300

Framework Versions

• Python: 3.11.11
• Sentence Transformers: 3.4.1
• Transformers: 4.48.3
• PyTorch: 2.5.1+cu124
• Accelerate: 1.3.0
• Datasets: 3.3.2
• Tokenizers: 0.21.0

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",
}

CoSENTLoss

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}