README.md

---
base_model: indobenchmark/indobert-base-p1
datasets: []
language: []
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:12000
- loss:MegaBatchMarginLoss
widget:
- source_sentence: Awalnya merupakan singkatan dari John's Macintosh Project.
  sentences:
  - Sebuah formasi yang terdiri dari sekitar 50 petugas Polisi Baltimore akhirnya
    menempatkan diri mereka di antara para perusuh dan milisi, memungkinkan Massachusetts
    ke-6 untuk melanjutkan ke Stasiun Camden.
  - Mengecat luka dapat melindungi dari jamur dan hama.
  - Dulunya merupakan singkatan dari John's Macintosh Project.
- source_sentence: Boueiz berprofesi sebagai pengacara.
  sentences:
  - Mereka juga gagal mengembangkan Water Cooperation Quotient yang baru.
  - Pada Pemilu 1970, ia ikut serta dari Partai Persatuan Nasional namun dikalahkan.
  - Seorang pengacara berprofesi sebagai Boueiz.
- source_sentence: Fakultas Studi Oriental memiliki seorang profesor.
  sentences:
  - Di tempat lain di New Mexico, LAHS terkadang dianggap sebagai sekolah untuk orang
    kaya.
  - Laporan lain juga menunjukkan kandungannya lebih rendah dari 0,1% di Australia.
  - Profesor tersebut merupakan bagian dari Fakultas Studi Oriental.
- source_sentence: Hal ini terjadi di sejumlah negara, termasuk Ethiopia, Republik
    Demokratik Kongo, dan Afrika Selatan.
  sentences:
  - Hal ini diketahui terjadi di Eritrea, Ethiopia, Kongo, Tanzania, Namibia dan Afrika
    Selatan.
  - Gugus amil digantikan oleh gugus pentil.
  - Dan saya beritahu Anda sesuatu, itu tidak adil.
- source_sentence: Ini adalah wilayah sosial-ekonomi yang lebih rendah.
  sentences:
  - Ini adalah bengkel perbaikan mobil terbaru yang masih beroperasi di kota.
  - Zelinsky hanya berteori bahwa tidak ada tiga bilangan bulat berurutan yang semuanya
    dapat difaktorkan ulang.
  - Ini adalah wilayah sosial-ekonomi yang lebih tinggi.
model-index:
- name: SentenceTransformer based on indobenchmark/indobert-base-p1
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: str dev
      type: str-dev
    metrics:
    - type: pearson_cosine
      value: 0.45499177580198114
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.47824954773877343
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.5063760846250573
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.49835693711719375
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.5062153453050553
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.4982327535492364
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.27097056415300647
      name: Pearson Dot
    - type: spearman_dot
      value: 0.25179460239023077
      name: Spearman Dot
    - type: pearson_max
      value: 0.5063760846250573
      name: Pearson Max
    - type: spearman_max
      value: 0.49835693711719375
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: str test
      type: str-test
    metrics:
    - type: pearson_cosine
      value: 0.47495139518851825
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.5059515739122313
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.50154011084872
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.5058071904463332
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.5028237271275693
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.5061159996491946
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.3250041946830172
      name: Pearson Dot
    - type: spearman_dot
      value: 0.31627719040314917
      name: Spearman Dot
    - type: pearson_max
      value: 0.5028237271275693
      name: Pearson Max
    - type: spearman_max
      value: 0.5061159996491946
      name: Spearman Max
---

# SentenceTransformer based on indobenchmark/indobert-base-p1

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1). 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-p1](https://huggingface.co/indobenchmark/indobert-base-p1) <!-- at revision c2cd0b51ddce6580eb35263b39b0a1e5fb0a39e2 -->
- **Maximum Sequence Length:** 32 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': 32, '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("damand2061/negasibert-mbm")
# Run inference
sentences = [
    'Ini adalah wilayah sosial-ekonomi yang lebih rendah.',
    'Ini adalah wilayah sosial-ekonomi yang lebih tinggi.',
    'Zelinsky hanya berteori bahwa tidak ada tiga bilangan bulat berurutan yang semuanya dapat difaktorkan ulang.',
]
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]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

## Evaluation

### Metrics

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

| Metric             | Value      |
|:-------------------|:-----------|
| pearson_cosine     | 0.455      |
| spearman_cosine    | 0.4782     |
| pearson_manhattan  | 0.5064     |
| spearman_manhattan | 0.4984     |
| pearson_euclidean  | 0.5062     |
| spearman_euclidean | 0.4982     |
| pearson_dot        | 0.271      |
| spearman_dot       | 0.2518     |
| pearson_max        | 0.5064     |
| **spearman_max**   | **0.4984** |

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

| Metric             | Value      |
|:-------------------|:-----------|
| pearson_cosine     | 0.475      |
| spearman_cosine    | 0.506      |
| pearson_manhattan  | 0.5015     |
| spearman_manhattan | 0.5058     |
| pearson_euclidean  | 0.5028     |
| spearman_euclidean | 0.5061     |
| pearson_dot        | 0.325      |
| spearman_dot       | 0.3163     |
| pearson_max        | 0.5028     |
| **spearman_max**   | **0.5061** |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 12,000 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                        | sentence_1                                                                        |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            |
  | details | <ul><li>min: 5 tokens</li><li>mean: 14.84 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.83 tokens</li><li>max: 32 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                                               | sentence_1                                                                                                          |
  |:-------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------|
  | <code>Pusat Peringatan Topan Gabungan (JTWC) juga mengeluarkan peringatan dalam kapasitas tidak resmi.</code>            | <code>Pusat Peringatan Topan Gabungan (JTWC) hanya mengeluarkan peringatan dalam kapasitas yang tidak resmi.</code> |
  | <code>DNP komersial digunakan sebagai antiseptik dan pestisida bioakumulasi non-selektif.</code>                         | <code>DNP komersial tidak dapat digunakan sebagai antiseptik atau pestisida bioakumulasi non-selektif.</code>       |
  | <code>Kuncian tulang belakang dan kuncian serviks diperbolehkan dan wajib dalam kompetisi jiu-jitsu Brasil IBJJF.</code> | <code>Kuncian tulang belakang dan kuncian serviks dilarang dalam kompetisi jiu-jitsu Brasil IBJJF.</code>           |
* Loss: [<code>MegaBatchMarginLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#megabatchmarginloss)

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

- `per_device_train_batch_size`: 500
- `per_device_eval_batch_size`: 500
- `num_train_epochs`: 5
- `multi_dataset_batch_sampler`: round_robin

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 500
- `per_device_eval_batch_size`: 500
- `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`: 5
- `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
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch | Step | str-dev_spearman_max | str-test_spearman_max |
|:-----:|:----:|:--------------------:|:---------------------:|
| 1.0   | 24   | 0.4904               | 0.5030                |
| 2.0   | 48   | 0.4905               | 0.5036                |
| 3.0   | 72   | 0.4947               | 0.5041                |
| 4.0   | 96   | 0.4963               | 0.5061                |
| 5.0   | 120  | 0.4984               | 0.5061                |


### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.1
- Transformers: 4.44.0
- PyTorch: 2.4.0
- Accelerate: 0.33.0
- Datasets: 2.21.0
- 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",
}
```

#### MegaBatchMarginLoss
```bibtex
@inproceedings{wieting-gimpel-2018-paranmt,
    title = "{P}ara{NMT}-50{M}: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations",
    author = "Wieting, John and Gimpel, Kevin",
    editor = "Gurevych, Iryna and Miyao, Yusuke",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P18-1042",
    doi = "10.18653/v1/P18-1042",
    pages = "451--462",
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->