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Initial Release: Indonesian Embedding Small with PyTorch and ONNX variants...
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 ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:10554
- loss:CosineSimilarityLoss
base_model: LazarusNLP/all-indo-e5-small-v4
widget:
- source_sentence: Menggunakan sunscreen setiap hari
sentences:
- Seorang anak laki-laki yang tampak sakit disentuh wajahnya oleh seorang balita.
- 'Warga Hispanik secara resmi telah menyalip warga Amerika keturunan Afrika sebagai
kelompok minoritas terbesar di AS
menurut laporan yang dirilis oleh Biro Sensus AS.'
- Tidak pernah menggunakan sunscreen
- source_sentence: Sering membeli makanan siap saji melalui aplikasi
sentences:
- Provinsi ini memiliki angka kepadatan penduduk 38 jiwa/km².
- Kadang membeli makanan siap saji melalui aplikasi
- Seorang pria sedang melakukan trik kartu.
- source_sentence: University of Michigan hari ini merilis kebijakan penerimaan mahasiswa
baru setelah Mahkamah Agung AS membatalkan cara penerimaan mahasiswa baru yang
sebelumnya.
sentences:
- '"Mereka telah memblokir semua tanaman bio baru karena ketakutan yang tidak berdasar
dan tidak ilmiah," kata Bush.'
- Jarang membeli kopi Kenangan
- University of Michigan berencana untuk merilis kebijakan penerimaan mahasiswa
baru pada hari Kamis setelah persyaratan penerimaannya ditolak oleh Mahkamah Agung
AS pada bulan Juni.
- source_sentence: pakar non-proliferasi di institut internasional untuk studi strategis
mark fitzpatrick menyatakan bahwa laporan IAEA - memiliki tenor yang sangat kuat.
sentences:
- Pernah membeli kopi Starbucks
- rekan senior di institut internasional untuk studi strategis mark fitzpatrick
menyatakan bahwa - rencana badan energi atom internasional adalah dangkal.
- Korea Utara mengusulkan pembicaraan tingkat tinggi dengan AS
- source_sentence: Palestina dan Yordania koordinasikan sikap dalam perundingan damai
sentences:
- Petinggi Hamas bantah Gaza dan PA berkoordinasi dalam perundingan damai
- Tidak pernah memesan makanan lewat aplikasi
- Kereta api yang melaju di atas rel.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on LazarusNLP/all-indo-e5-small-v4
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts indo detailed
type: sts-indo-detailed
metrics:
- type: pearson_cosine
value: 0.8612625897174441
name: Pearson Cosine
- type: spearman_cosine
value: 0.8586969176298713
name: Spearman Cosine
---
# SentenceTransformer based on LazarusNLP/all-indo-e5-small-v4
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [LazarusNLP/all-indo-e5-small-v4](https://huggingface.co/LazarusNLP/all-indo-e5-small-v4). 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](https://huggingface.co/LazarusNLP/all-indo-e5-small-v4) <!-- at revision 239ef03629c10bce80ea9e557255f249a542dece -->
- **Maximum Sequence Length:** 384 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False, 'architecture': '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:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'Palestina dan Yordania koordinasikan sikap dalam perundingan damai',
'Petinggi Hamas bantah Gaza dan PA berkoordinasi dalam perundingan damai',
'Kereta api yang melaju di atas rel.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000, 0.5014, -0.0652],
# [ 0.5014, 1.0000, -0.0518],
# [-0.0652, -0.0518, 1.0000]])
```
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## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-indo-detailed`
* Evaluated with <code>__main__.DetailedEmbeddingSimilarityEvaluator</code>
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8613 |
| **spearman_cosine** | **0.8587** |
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 10,554 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 14.45 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.19 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:-------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------|
| <code>Tidak pernah mengisi saldo ShopeePay</code> | <code>Tidak pernah mengisi saldo GoPay</code> | <code>0.0</code> |
| <code>PM Turki mendesak untuk mengakhiri protes di Istanbul</code> | <code>Polisi Turki menembakkan gas air mata ke arah pengunjuk rasa di Istanbul</code> | <code>0.56</code> |
| <code>Dua ekor kucing sedang melihat ke arah jendela.</code> | <code>Seekor kucing putih yang sedang melihat ke luar jendela.</code> | <code>0.5199999809265137</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 6
- `per_device_eval_batch_size`: 6
- `num_train_epochs`: 7
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 6
- `per_device_eval_batch_size`: 6
- `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`: 7
- `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}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `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`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | sts-indo-detailed_spearman_cosine |
|:------:|:----:|:-------------:|:---------------------------------:|
| 0.0569 | 100 | - | 0.8225 |
| 0.1137 | 200 | - | 0.8261 |
| 0.1706 | 300 | - | 0.8263 |
| 0.2274 | 400 | - | 0.8259 |
| 0.2843 | 500 | 0.0764 | 0.8273 |
| 0.3411 | 600 | - | 0.8305 |
| 0.3980 | 700 | - | 0.8319 |
| 0.4548 | 800 | - | 0.8341 |
| 0.5117 | 900 | - | 0.8345 |
| 0.5685 | 1000 | 0.0445 | 0.8362 |
| 0.6254 | 1100 | - | 0.8384 |
| 0.6822 | 1200 | - | 0.8391 |
| 0.7391 | 1300 | - | 0.8464 |
| 0.7959 | 1400 | - | 0.8475 |
| 0.8528 | 1500 | 0.0372 | 0.8471 |
| 0.9096 | 1600 | - | 0.8477 |
| 0.9665 | 1700 | - | 0.8458 |
| 1.0 | 1759 | - | 0.8464 |
| 1.0233 | 1800 | - | 0.8443 |
| 1.0802 | 1900 | - | 0.8455 |
| 1.1370 | 2000 | 0.0316 | 0.8481 |
| 1.1939 | 2100 | - | 0.8447 |
| 1.2507 | 2200 | - | 0.8473 |
| 1.3076 | 2300 | - | 0.8474 |
| 1.3644 | 2400 | - | 0.8449 |
| 1.4213 | 2500 | 0.0281 | 0.8515 |
| 1.4781 | 2600 | - | 0.8498 |
| 1.5350 | 2700 | - | 0.8506 |
| 1.5918 | 2800 | - | 0.8546 |
| 1.6487 | 2900 | - | 0.8534 |
| 1.7055 | 3000 | 0.0271 | 0.8512 |
| 1.7624 | 3100 | - | 0.8493 |
| 1.8192 | 3200 | - | 0.8499 |
| 1.8761 | 3300 | - | 0.8523 |
| 1.9329 | 3400 | - | 0.8518 |
| 1.9898 | 3500 | 0.0258 | 0.8529 |
| 2.0 | 3518 | - | 0.8535 |
| 2.0466 | 3600 | - | 0.8546 |
| 2.1035 | 3700 | - | 0.8526 |
| 2.1603 | 3800 | - | 0.8548 |
| 2.2172 | 3900 | - | 0.8504 |
| 2.2740 | 4000 | 0.0222 | 0.8535 |
| 2.3309 | 4100 | - | 0.8533 |
| 2.3877 | 4200 | - | 0.8538 |
| 2.4446 | 4300 | - | 0.8518 |
| 2.5014 | 4400 | - | 0.8515 |
| 2.5583 | 4500 | 0.021 | 0.8515 |
| 2.6151 | 4600 | - | 0.8529 |
| 2.6720 | 4700 | - | 0.8548 |
| 2.7288 | 4800 | - | 0.8552 |
| 2.7857 | 4900 | - | 0.8542 |
| 2.8425 | 5000 | 0.0209 | 0.8571 |
| 2.8994 | 5100 | - | 0.8552 |
| 2.9562 | 5200 | - | 0.8553 |
| 3.0 | 5277 | - | 0.8552 |
| 3.0131 | 5300 | - | 0.8560 |
| 3.0699 | 5400 | - | 0.8531 |
| 3.1268 | 5500 | 0.0199 | 0.8491 |
| 3.1836 | 5600 | - | 0.8515 |
| 3.2405 | 5700 | - | 0.8520 |
| 3.2973 | 5800 | - | 0.8547 |
| 3.3542 | 5900 | - | 0.8558 |
| 3.4110 | 6000 | 0.0182 | 0.8560 |
| 3.4679 | 6100 | - | 0.8561 |
| 3.5247 | 6200 | - | 0.8562 |
| 3.5816 | 6300 | - | 0.8547 |
| 3.6384 | 6400 | - | 0.8547 |
| 3.6953 | 6500 | 0.0171 | 0.8561 |
| 3.7521 | 6600 | - | 0.8563 |
| 3.8090 | 6700 | - | 0.8555 |
| 3.8658 | 6800 | - | 0.8562 |
| 3.9227 | 6900 | - | 0.8587 |
### Framework Versions
- Python: 3.11.13
- Sentence Transformers: 5.1.0
- Transformers: 4.56.0
- PyTorch: 2.8.0
- Accelerate: 1.10.1
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@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",
}
```
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