README.md

---
base_model: indobenchmark/indobert-large-p2
datasets:
- quarkss/stsb-indo-mt
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:5749
- loss:CosineSimilarityLoss
widget:
- source_sentence: Dua ekor anjing berenang di kolam renang.
  sentences:
  - Anjing-anjing sedang berenang di kolam renang.
  - Seekor binatang sedang berjalan di atas tanah.
  - Seorang pria sedang menyeka pinggiran mangkuk.
- source_sentence: Seorang anak perempuan sedang mengiris mentega menjadi dua bagian.
  sentences:
  - Seorang wanita sedang mengiris tahu.
  - Dua orang berkelahi.
  - Seorang pria sedang menari.
- source_sentence: Seorang gadis sedang makan kue mangkuk.
  sentences:
  - Seorang pria sedang mengiris bawang putih dengan alat pengiris mandolin.
  - Seorang pria sedang memotong dan memotong bawang.
  - Seorang wanita sedang makan kue mangkuk.
- source_sentence: Sebuah helikopter mendarat di landasan helikopter.
  sentences:
  - Seorang pria sedang mengiris mentimun.
  - Seorang pria sedang memotong batang pohon dengan kapak.
  - Sebuah helikopter mendarat.
- source_sentence: Seorang pria sedang berjalan dengan seekor kuda.
  sentences:
  - Seorang pria sedang menuntun seekor kuda dengan tali kekang.
  - Seorang pria sedang menembakkan pistol.
  - Seorang wanita sedang memetik tomat.
model-index:
- name: SentenceTransformer based on indobenchmark/indobert-large-p2
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: pearson_cosine
      value: 0.8691840566814281
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8676618157111291
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8591936899214765
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8625729388794413
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8599101625523397
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8632992102966184
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8440663965451926
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8392116432595296
      name: Spearman Dot
    - type: pearson_max
      value: 0.8691840566814281
      name: Pearson Max
    - type: spearman_max
      value: 0.8676618157111291
      name: Spearman Max
    - type: pearson_cosine
      value: 0.8401688802461491
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8365597846163649
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8276067064758832
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8315689286193226
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8277930159560367
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.831557090168861
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8170329546065831
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8083098402255348
      name: Spearman Dot
    - type: pearson_max
      value: 0.8401688802461491
      name: Pearson Max
    - type: spearman_max
      value: 0.8365597846163649
      name: Spearman Max
---

# SentenceTransformer based on indobenchmark/indobert-large-p2

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

## STSB Test
| Model                                   |   Spearman Correlation |
|:----------------------------------------|-----------------------:|
| quarkss/indobert-large-stsb             |                 0.8366 |
| quarkss/indobert-base-stsb              |                 0.8123 |
| sentence-transformers/all-MiniLM-L6-v2  |                 0.5952 |
| indobenchmark/indobert-large-p2         |                 0.5673 |
| sentence-transformers/all-mpnet-base-v2 |                 0.5531 |
| sentence-transformers/stsb-bert-base    |                 0.5349 |
| indobenchmark/indobert-base-p2          |                 0.5309 |

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [indobenchmark/indobert-large-p2](https://huggingface.co/indobenchmark/indobert-large-p2) <!-- at revision 4b280c3bfcc1ed2d6b4589be5c876076b7d73568 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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("quarkss/indobert-large-stsb")
# Run inference
sentences = [
    'Seorang pria sedang berjalan dengan seekor kuda.',
    'Seorang pria sedang menuntun seekor kuda dengan tali kekang.',
    'Seorang pria sedang menembakkan pistol.',
]
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.

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

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## Evaluation

### Metrics

#### Semantic Similarity

* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8692     |
| **spearman_cosine** | **0.8677** |
| pearson_manhattan   | 0.8592     |
| spearman_manhattan  | 0.8626     |
| pearson_euclidean   | 0.8599     |
| spearman_euclidean  | 0.8633     |
| pearson_dot         | 0.8441     |
| spearman_dot        | 0.8392     |
| pearson_max         | 0.8692     |
| spearman_max        | 0.8677     |

#### Semantic Similarity

* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric             | Value      |
|:-------------------|:-----------|
| pearson_cosine     | 0.8402     |
| spearman_cosine    | 0.8366     |
| pearson_manhattan  | 0.8276     |
| spearman_manhattan | 0.8316     |
| pearson_euclidean  | 0.8278     |
| spearman_euclidean | 0.8316     |
| pearson_dot        | 0.817      |
| spearman_dot       | 0.8083     |
| pearson_max        | 0.8402     |
| **spearman_max**   | **0.8366** |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 5,749 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: 9.65 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 9.59 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                              | sentence2                                                                                | score             |
  |:-----------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|:------------------|
  | <code>Sebuah pesawat sedang lepas landas.</code>                       | <code>Sebuah pesawat terbang sedang lepas landas.</code>                                 | <code>1.0</code>  |
  | <code>Seorang pria sedang memainkan seruling besar.</code>             | <code>Seorang pria sedang memainkan seruling.</code>                                     | <code>0.76</code> |
  | <code>Seorang pria sedang mengoleskan keju parut di atas pizza.</code> | <code>Seorang pria sedang mengoleskan keju parut di atas pizza yang belum matang.</code> | <code>0.76</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`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `fp16`: True

#### 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`: 2e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 5
- `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`: 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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | spearman_cosine | spearman_max |
|:------:|:----:|:-------------:|:---------------:|:------------:|
| 0.2778 | 100  | 0.0867        | -               | -            |
| 0.5556 | 200  | 0.0351        | -               | -            |
| 0.8333 | 300  | 0.0303        | -               | -            |
| 1.1111 | 400  | 0.0202        | -               | -            |
| 1.3889 | 500  | 0.0154        | 0.8612          | -            |
| 1.6667 | 600  | 0.0136        | -               | -            |
| 1.9444 | 700  | 0.0145        | -               | -            |
| 2.2222 | 800  | 0.0082        | -               | -            |
| 2.5    | 900  | 0.0072        | -               | -            |
| 2.7778 | 1000 | 0.0068        | 0.8660          | -            |
| 3.0556 | 1100 | 0.0065        | -               | -            |
| 3.3333 | 1200 | 0.0044        | -               | -            |
| 3.6111 | 1300 | 0.0044        | -               | -            |
| 3.8889 | 1400 | 0.0045        | -               | -            |
| 4.1667 | 1500 | 0.0038        | 0.8677          | -            |
| 4.4444 | 1600 | 0.0038        | -               | -            |
| 4.7222 | 1700 | 0.0035        | -               | -            |
| 5.0    | 1800 | 0.0034        | -               | 0.8366       |


### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.0.1+cu117
- Accelerate: 0.32.1
- Datasets: 2.17.0
- 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",
}
```

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