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---
language:
- id
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:6198
- loss:CoSENTLoss
base_model: BAAI/bge-m3
widget:
- source_sentence: Seekor kucing hitam dan putih yang sedang bermain dengan keranjang
wol.
sentences:
- Dua ekor anjing berlari melintasi lapangan berumput.
- Seorang pria mengiris bawang.
- Seekor kucing hitam dan putih yang sedang berbaring di atas selimut.
- source_sentence: Bintang-bintang memang berotasi, tapi itu bukan penyebab kestabilannya.
sentences:
- Seorang pria sedang bernyanyi dan memainkan gitar.
- Tingkat pertumbuhan Uni Soviet selama tahun 50-an tidak terlalu tinggi.
- Bintang berotasi karena momentum sudut gas yang membentuknya.
- source_sentence: Hal penting yang saya coba ingat adalah, hanya memperhatikan.
sentences:
- Tiga orang wanita sedang duduk di dekat dinding.
- Saya telah membaca tentang topik ini sejak saya mengajukan pertanyaan ini.
- Untuk melatih diri Anda menggunakan pintasan keyboard, cabutlah mouse Anda selama
beberapa hari.
- source_sentence: Mari kita asumsikan data untuk gugus bola setara dengan data M13.
sentences:
- Wanita itu mengiris dagingnya.
- Sebuah laptop dan PC di stasiun kerja.
- Gugus bola menempati tempat yang menarik dalam spektrum sistem bintang komposit.
- source_sentence: 'Jawaban singkatnya adalah: kita terbuat dari "materi" yang disumbangkan
oleh banyak bintang.'
sentences:
- Sebuah band sedang bermain di atas panggung.
- Sangat tidak mungkin bahwa kita terbuat dari benda-benda yang hanya terbuat dari
satu bintang.
- Seorang wanita sedang mengiris brokoli.
datasets:
- Pustekhan-ITB/stsb-indo-edu
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on BAAI/bge-m3
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: stsb indo edu dev
type: stsb-indo-edu-dev
metrics:
- type: pearson_cosine
value: 0.8432609269312235
name: Pearson Cosine
- type: spearman_cosine
value: 0.8580118610725878
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: stsb indo edu test
type: stsb-indo-edu-test
metrics:
- type: pearson_cosine
value: 0.8442709665997649
name: Pearson Cosine
- type: spearman_cosine
value: 0.8602630711004111
name: Spearman Cosine
---
# SentenceTransformer based on BAAI/bge-m3
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) on the [stsb-indo-edu](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu) dataset. It maps sentences & paragraphs to a 1024-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:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [stsb-indo-edu](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu)
- **Language:** id
<!-- - **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': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, '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})
(2): Normalize()
)
```
## 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("Pustekhan-ITB/indoedubert-bge-m3-exp2")
# 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, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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You can finetune this model on your own dataset.
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## Evaluation
### Metrics
#### Semantic Similarity
* Datasets: `stsb-indo-edu-dev` and `stsb-indo-edu-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | stsb-indo-edu-dev | stsb-indo-edu-test |
|:--------------------|:------------------|:-------------------|
| pearson_cosine | 0.8433 | 0.8443 |
| **spearman_cosine** | **0.858** | **0.8603** |
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## Training Details
### Training Dataset
#### stsb-indo-edu
* Dataset: [stsb-indo-edu](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu) at [2c5aa12](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu/tree/2c5aa12013e2367fba1b91e63f0466f77f53ac6d)
* Size: 6,198 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 10.95 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.81 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.46</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------|:------------------|
| <code>Pelajaran menari daerah membantu siswa SD melestarikan kebudayaan lokal</code> | <code>Tarian ini sering dipentaskan saat perayaan hari besar</code> | <code>0.76</code> |
| <code>Sebelum ujian sekolah, guru memberikan bimbingan belajar tambahan secara gratis</code> | <code>Upaya ini agar seluruh siswa siap menghadapi ujian</code> | <code>0.85</code> |
| <code>Beberapa SD terletak di daerah pegunungan, sehingga siswa harus berjalan kaki cukup jauh</code> | <code>Ini melatih kemandirian dan fisik yang kuat</code> | <code>0.63</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Evaluation Dataset
#### stsb-indo-edu
* Dataset: [stsb-indo-edu](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu) at [2c5aa12](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu/tree/2c5aa12013e2367fba1b91e63f0466f77f53ac6d)
* Size: 1,536 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 15.96 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.97 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------|:------------------|
| <code>Seorang pria dengan topi keras sedang menari.</code> | <code>Seorang pria yang mengenakan topi keras sedang menari.</code> | <code>1.0</code> |
| <code>Seorang anak kecil sedang menunggang kuda.</code> | <code>Seorang anak sedang menunggang kuda.</code> | <code>0.95</code> |
| <code>Seorang pria sedang memberi makan seekor tikus kepada seekor ular.</code> | <code>Pria itu sedang memberi makan seekor tikus kepada ular.</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### 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`: 32
- `per_device_eval_batch_size`: 32
- `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`: 1
- `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
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | stsb-indo-edu-dev_spearman_cosine | stsb-indo-edu-test_spearman_cosine |
|:------:|:----:|:-------------:|:---------------:|:---------------------------------:|:----------------------------------:|
| -1 | -1 | - | - | 0.8096 | - |
| 0.5155 | 100 | 6.0081 | 5.7898 | 0.8580 | - |
| -1 | -1 | - | - | - | 0.8603 |
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.5.1
- Accelerate: 1.3.0
- Datasets: 3.3.0
- Tokenizers: 0.21.0
## 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",
}
```
#### CoSENTLoss
```bibtex
@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},
}
```
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