README.md

---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:10330
- loss:MultipleNegativesRankingLoss
base_model: indobenchmark/indobert-base-p2
datasets: []
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: Pura Ulun Danu terletak sekitar 56 kilometer dari Kota Denpasar.
  sentences:
  - Dalam tujuh bulan kehamilan, organ tubuh bayi sudah sempurna.
  - Dokter Adeline menjelaskan aturan-aturan agar diabetisi aman berpuasa.
  - Pura Ulun Danu terletak sekitar satu jam perjalanan dari Kota Denpasar.
- source_sentence: Di luar ujung barat laut, taiga dominan, mencakup bagian besar
    dari seluruh Siberia.
  sentences:
  - Banyak keraguan mengenai tanggal kelahiran Gaudapa.
  - Sebagian besar Siberia terletak di ujung barat laut,.
  - Maia menyaksikan balapan tanpa alasan.
- source_sentence: Widodo Cahyono Putro adalah seorang pelatih dan pemain sepak bola
    legendaris Indonesia.
  sentences:
  - Ia berjanji untuk jatuh di lubang yang sama.
  - Pemain sepak bola legendaris pasti menjadi pelatih sepak bola.
  - Nazaruddin menegaskan bahwa mantan Wakil Ketua Komisi II DPR itu menerima uang
    dari proyek e-KTP sebesar $500 ribu.
- source_sentence: Salah satunya seorang lelaki yang sedang memakan permen karet yang
    dengan paksa dikeluarkan dari mulutnya.
  sentences:
  - Charles Leclerc gagal menjadi juara dunia F2.
  - Pendukung pembrontakan Cina sudah tidak ada.
  - Lelaki itu bukan salah satunya.
- source_sentence: Tumenggung Wirapraja setelah mangkat dimakamkan di Kebon Alas Warudoyong,
    Kecamatan Panumbangan, Kabupaten Ciamis.
  sentences:
  - Peristiwa Pemberontakan Besar di Minahasa memiliki dampak besar pada tentara Sekutu.
  - Di hari libur ini, Pengunjung semua taman nasional tidak dibebaskan biaya.
  - Tumenggung Wirapraja dikremasi setelah dipastikan mangkat dan abunya kemudian
    dilarungkan ke Pantai Laut Selatan.
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on indobenchmark/indobert-base-p2
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: -0.05296221890135024
      name: Pearson Cosine
    - type: spearman_cosine
      value: -0.06107163627723088
      name: Spearman Cosine
    - type: pearson_manhattan
      value: -0.06399377304712585
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: -0.06835801919486152
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: -0.0642574675392147
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: -0.06906447787846218
      name: Spearman Euclidean
    - type: pearson_dot
      value: -0.024528943319169508
      name: Pearson Dot
    - type: spearman_dot
      value: -0.024236369255517205
      name: Spearman Dot
    - type: pearson_max
      value: -0.024528943319169508
      name: Pearson Max
    - type: spearman_max
      value: -0.024236369255517205
      name: Spearman Max
---

# SentenceTransformer based on indobenchmark/indobert-base-p2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2). It maps 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.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) <!-- at revision 94b4e0a82081fa57f227fcc2024d1ea89b57ac1f -->
- **Maximum Sequence Length:** 75 tokens
- **Output Dimensionality:** 768 tokens
- **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': 75, 'do_lower_case': False}) with Transformer model: BertModel 
  (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:

```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("Hvare/Athena-indobert-finetuned-indonli")
# Run inference
sentences = [
    'Tumenggung Wirapraja setelah mangkat dimakamkan di Kebon Alas Warudoyong, Kecamatan Panumbangan, Kabupaten Ciamis.',
    'Tumenggung Wirapraja dikremasi setelah dipastikan mangkat dan abunya kemudian dilarungkan ke Pantai Laut Selatan.',
    'Di hari libur ini, Pengunjung semua taman nasional tidak dibebaskan biaya.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

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## Evaluation

### Metrics

#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric             | Value       |
|:-------------------|:------------|
| pearson_cosine     | -0.053      |
| spearman_cosine    | -0.0611     |
| pearson_manhattan  | -0.064      |
| spearman_manhattan | -0.0684     |
| pearson_euclidean  | -0.0643     |
| spearman_euclidean | -0.0691     |
| pearson_dot        | -0.0245     |
| spearman_dot       | -0.0242     |
| pearson_max        | -0.0245     |
| **spearman_max**   | **-0.0242** |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 10,330 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                                                                            | int                                                                |
  | details | <ul><li>min: 11 tokens</li><li>mean: 29.47 tokens</li><li>max: 75 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.25 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>0: ~35.90%</li><li>1: ~32.00%</li><li>2: ~32.10%</li></ul> |
* Samples:
  | sentence_0                                                                                                          | sentence_1                                                                | label          |
  |:--------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------|:---------------|
  | <code>"" "Akan ada protes dan hal-hal lain, semua nya sudah direncanakan," "ungkap oposisi kepada El Mundo."</code> | <code>Protes dan hal-hal lain sudah direncanakan.</code>                  | <code>0</code> |
  | <code>Tak jarang, bangun kesiangan pun jadi alasan untuk tak berolahraga.</code>                                    | <code>Salah satu alasan tidak berolahraga adalah bangun kesiangan.</code> | <code>0</code> |
  | <code>Namun, saingannya Prabowo Subianto juga mendeklarasikan kemenangan, membuat orang Indonesia bingung.</code>   | <code>Prabowo menerima bahwa Dia kalah.</code>                            | <code>2</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `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`: 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
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch  | Step | Training Loss | sts-dev_spearman_max |
|:------:|:----:|:-------------:|:--------------------:|
| 0.0991 | 64   | -             | -0.0411              |
| 0.1981 | 128  | -             | -0.0426              |
| 0.2972 | 192  | -             | -0.0419              |
| 0.3963 | 256  | -             | -0.0425              |
| 0.4954 | 320  | -             | -0.0384              |
| 0.5944 | 384  | -             | -0.0260              |
| 0.6935 | 448  | -             | -0.0216              |
| 0.7740 | 500  | 0.0531        | -                    |
| 0.7926 | 512  | -             | -0.0243              |
| 0.8916 | 576  | -             | -0.0241              |
| 0.9907 | 640  | -             | -0.0242              |
| 1.0    | 646  | -             | -0.0242              |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.0+cu121
- Accelerate: 0.31.0
- Datasets: 2.19.2
- Tokenizers: 0.19.1

## 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",
}
```

#### MultipleNegativesRankingLoss
```bibtex
@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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