sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:6915 loss:SoftmaxLoss id dataset:afaji/indonli arxiv:1908.10084 base_model:indobenchmark/indobert-base-p2 base_model:finetune:indobenchmark/indobert-base-p2 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: indobenchmark/indobert-base-p2
datasets:
- afaji/indonli
language:
- id
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:6915
- loss:SoftmaxLoss
widget:
- source_sentence: Pesta Olahraga Asia Tenggara atau Southeast Asian Games, biasa
    disingkat SEA Games, adalah ajang olahraga yang diadakan setiap dua tahun dan
    melibatkan 11 negara Asia Tenggara.
  sentences:
  - Sekarang tahun 2017.
  - Warna kulit tidak mempengaruhi waktu berjemur yang baik untuk mengatifkan pro-vitamin
    D3.
  - Pesta Olahraga Asia Tenggara diadakan setiap tahun.
- source_sentence: Menjalani aktivitas Ramadhan di tengah wabah Corona tentunya tidak
    mudah.
  sentences:
  - Tidak ada observasi yang pernah dilansir oleh Business Insider.
  - Wabah Corona membuat aktivitas Ramadhan tidak mudah dijalani.
  - Piala Sudirman pertama digelar pada tahun 1989.
- source_sentence: Dalam bidang politik, partai ini memperjuangkan agar kekuasaan
    sepenuhnya berada di tangan rakyat.
  sentences:
  - Galileo tidak berhasil mengetes hasil dari Hukum Inert.
  - Kudeta 14 Februari 1946 gagal merebut kekuasaan Belanda.
  - Partai ini berusaha agar kekuasaan sepenuhnya berada di tangan rakyat.
- source_sentence: Keluarga mendiang Prince menuduh layanan musik streaming Tidal
    memasukkan karya milik sang penyanyi legendaris tanpa izin .
  sentences:
  - Rosier adalah pelayan setia Lord Voldemort.
  - Bangunan ini digunakan untuk penjualan.
  - Keluarga mendiang Prince sudah memberi izin kepada TImbal untuk menggunakan lagu
    milik Prince.
- source_sentence: Tujuan dari acara dengar pendapat CRTC adalah untuk mengumpulkan
    respons dari pada pemangku kepentingan industri ini dan dari masyarakat umum.
  sentences:
  - Pembuat Rooms hanya bisa membuat meeting yang terbuka.
  - Masyarakat umum dilibatkan untuk memberikan respon dalam acara dengar pendapat
    CRTC.
  - Eminem dirasa tidak akan memulai kembali kariernya tahun ini.
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: 0.6027226710720388
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.5828122556183322
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.5865637873532972
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.5646745853309448
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.5929829731938223
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.5686448857290627
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.5979483667153003
      name: Pearson Dot
    - type: spearman_dot
      value: 0.5928346499987958
      name: Spearman Dot
    - type: pearson_max
      value: 0.6027226710720388
      name: Pearson Max
    - type: spearman_max
      value: 0.5928346499987958
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test
      type: sts-test
    metrics:
    - type: pearson_cosine
      value: 0.29752953323287884
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.26804775967587074
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.2519074599400818
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.24226962957953038
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.26198515666148037
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.2449838180623571
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.32041136820361227
      name: Pearson Dot
    - type: spearman_dot
      value: 0.3038800429933888
      name: Spearman Dot
    - type: pearson_max
      value: 0.32041136820361227
      name: Pearson Max
    - type: spearman_max
      value: 0.3038800429933888
      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) on the [afaji/indonli](https://huggingface.co/datasets/afaji/indonli) dataset. 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:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [afaji/indonli](https://huggingface.co/datasets/afaji/indonli)
- **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': 512, '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("cassador/2bs8lr2")
# Run inference
sentences = [
    'Tujuan dari acara dengar pendapat CRTC adalah untuk mengumpulkan respons dari pada pemangku kepentingan industri ini dan dari masyarakat umum.',
    'Masyarakat umum dilibatkan untuk memberikan respon dalam acara dengar pendapat CRTC.',
    'Pembuat Rooms hanya bisa membuat meeting yang terbuka.',
]
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      | 0.6027     |
| **spearman_cosine** | **0.5828** |
| pearson_manhattan   | 0.5866     |
| spearman_manhattan  | 0.5647     |
| pearson_euclidean   | 0.593      |
| spearman_euclidean  | 0.5686     |
| pearson_dot         | 0.5979     |
| spearman_dot        | 0.5928     |
| pearson_max         | 0.6027     |
| spearman_max        | 0.5928     |

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

| Metric              | Value     |
|:--------------------|:----------|
| pearson_cosine      | 0.2975    |
| **spearman_cosine** | **0.268** |
| pearson_manhattan   | 0.2519    |
| spearman_manhattan  | 0.2423    |
| pearson_euclidean   | 0.262     |
| spearman_euclidean  | 0.245     |
| pearson_dot         | 0.3204    |
| spearman_dot        | 0.3039    |
| pearson_max         | 0.3204    |
| spearman_max        | 0.3039    |

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## Training Details

### Training Dataset

#### afaji/indonli

* Dataset: [afaji/indonli](https://huggingface.co/datasets/afaji/indonli)
* Size: 6,915 training samples
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | premise                                                                             | hypothesis                                                                        | label                                           |
  |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                              | string                                                                            | int                                             |
  | details | <ul><li>min: 12 tokens</li><li>mean: 29.26 tokens</li><li>max: 135 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.13 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>0: ~51.00%</li><li>1: ~49.00%</li></ul> |
* Samples:
  | premise                                                                                                                                                                    | hypothesis                                                               | label          |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------|:---------------|
  | <code>Presiden Joko Widodo (Jokowi) menyampaikan prediksi bahwa wabah virus Corona (COVID-19) di Indonesia akan selesai akhir tahun ini.</code>                            | <code>Prediksi akhir wabah tidak disampaikan Jokowi.</code>              | <code>0</code> |
  | <code>Meski biasanya hanya digunakan di fasilitas kesehatan, saat ini masker dan sarung tangan sekali pakai banyak dipakai di tingkat rumah tangga.</code>                 | <code>Masker sekali pakai banyak dipakai di tingkat rumah tangga.</code> | <code>1</code> |
  | <code>Seperti namanya, paket internet sahur Telkomsel ini ditujukan bagi pengguna yang menginginkan kuota ekstra, untuk menemani momen sahur sepanjang bulan puasa.</code> | <code>Paket internet sahur tidak ditujukan untuk saat sahur.</code>      | <code>0</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)

### Evaluation Dataset

#### afaji/indonli

* Dataset: [afaji/indonli](https://huggingface.co/datasets/afaji/indonli)
* Size: 1,556 evaluation samples
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | premise                                                                            | hypothesis                                                                        | label                                           |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                             | string                                                                            | int                                             |
  | details | <ul><li>min: 9 tokens</li><li>mean: 28.07 tokens</li><li>max: 179 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.15 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>0: ~47.90%</li><li>1: ~52.10%</li></ul> |
* Samples:
  | premise                                                                                                                                                                                                                                                                        | hypothesis                                                                   | label          |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------|:---------------|
  | <code>Manuskrip tersebut berisi tiga catatan yang menceritakan bagaimana peristiwa jatuhnya meteorit serta laporan kematian akibat kejadian tersebut seperti dilansir dari Science Alert, Sabtu (25/4/2020).</code>                                                            | <code>Manuskrip tersebut tidak mencatat laporan kematian.</code>             | <code>0</code> |
  | <code>Dilansir dari Business Insider, menurut observasi dari Mauna Loa Observatory di Hawaii pada karbon dioksida (CO2) di level mencapai 410 ppm tidak langsung memberikan efek pada pernapasan, karena tubuh manusia juga masih membutuhkan CO2 dalam kadar tertentu.</code> | <code>Tidak ada observasi yang pernah dilansir oleh Business Insider.</code> | <code>0</code> |
  | <code>Seorang wanita asal New York mengaku sangat benci air putih.</code>                                                                                                                                                                                                      | <code>Tidak ada orang dari New York yang membenci air putih.</code>          | <code>0</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)

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

- `eval_strategy`: epoch
- `learning_rate`: 2e-05
- `num_train_epochs`: 2
- `warmup_ratio`: 0.1
- `fp16`: True

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `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`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | loss   | sts-dev_spearman_cosine | sts-test_spearman_cosine |
|:------:|:----:|:-------------:|:------:|:-----------------------:|:------------------------:|
| 0      | 0    | -             | -      | 0.1277                  | -                        |
| 0.1156 | 100  | 0.6407        | -      | -                       | -                        |
| 0.2312 | 200  | 0.5119        | -      | -                       | -                        |
| 0.3468 | 300  | 0.5192        | -      | -                       | -                        |
| 0.4624 | 400  | 0.5104        | -      | -                       | -                        |
| 0.5780 | 500  | 0.5087        | -      | -                       | -                        |
| 0.6936 | 600  | 0.515         | -      | -                       | -                        |
| 0.8092 | 700  | 0.4965        | -      | -                       | -                        |
| 0.9249 | 800  | 0.4771        | -      | -                       | -                        |
| 1.0    | 865  | -             | 0.4387 | 0.5481                  | -                        |
| 1.0405 | 900  | 0.4122        | -      | -                       | -                        |
| 1.1561 | 1000 | 0.3551        | -      | -                       | -                        |
| 1.2717 | 1100 | 0.3332        | -      | -                       | -                        |
| 1.3873 | 1200 | 0.3297        | -      | -                       | -                        |
| 1.5029 | 1300 | 0.3292        | -      | -                       | -                        |
| 1.6185 | 1400 | 0.3646        | -      | -                       | -                        |
| 1.7341 | 1500 | 0.3375        | -      | -                       | -                        |
| 1.8497 | 1600 | 0.3257        | -      | -                       | -                        |
| 1.9653 | 1700 | 0.385         | -      | -                       | -                        |
| 2.0    | 1730 | -             | 0.4650 | 0.5828                  | 0.2680                   |


### 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: 2.20.0
- Tokenizers: 0.19.1

## Citation

### BibTeX

#### Sentence Transformers and SoftmaxLoss
```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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