sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:39836 loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:1705.00652 base_model:BAAI/bge-m3 base_model:finetune:BAAI/bge-m3 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-m3
datasets: []
language: []
library_name: sentence-transformers
metrics:
- cosine_accuracy
- dot_accuracy
- manhattan_accuracy
- euclidean_accuracy
- max_accuracy
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:39836
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Seorang pria bertopi biru dan rompi keselamatan oranye berdiri
    di persimpangan sambil memegang bendera.
  sentences:
  - Sekelompok orang menaiki eskalator, banyak dari mereka memegang payung.
  - Seseorang berpakaian agar mudah terlihat.
  - Seorang pria mengenakan topi keras oranye berdiri di persimpangan jalan.
- source_sentence: Dua anjing saling memandang di luar.
  sentences:
  - Ada dua anjing di luar.
  - Empat anjing saling memandang di dalam.
  - Seorang pria di luar gedung bata merah dengan kereta belanja, sepeda, dan lain-lain.
- source_sentence: Pria itu berdiri.
  sentences:
  - Seorang pria dan wanita duduk bersama di meja.
  - Orang-orang di pasar petani luar ruangan.
  - Seorang pria di kota di luar gedung berdiri di tangga.
- source_sentence: Seorang pria sedang tidur.
  sentences:
  - Seorang pria berselimut sedang tertidur di trotoar.
  - Manusia ditutupi spons beraneka warna.
  - Seorang pria tunawisma tertidur di trotoar.
- source_sentence: Orang-orang ada di luar.
  sentences:
  - Seorang pria berbaju kotak-kotak dan sandal putih sedang tertidur sambil membaca
    koran.
  - Orang-orang berjalan di luar dan mengenakan warna gelap.
  - Sekelompok orang sedang makan di sebuah restoran dengan mural seorang wanita sedang
    berbelanja di belakang mereka.
model-index:
- name: SentenceTransformer based on BAAI/bge-m3
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: model evaluation
      type: model-evaluation
    metrics:
    - type: cosine_accuracy
      value: 0.9636322566071832
      name: Cosine Accuracy
    - type: dot_accuracy
      value: 0.03636774339281681
      name: Dot Accuracy
    - type: manhattan_accuracy
      value: 0.9625028235825616
      name: Manhattan Accuracy
    - type: euclidean_accuracy
      value: 0.9636322566071832
      name: Euclidean Accuracy
    - type: max_accuracy
      value: 0.9636322566071832
      name: Max Accuracy
---

# 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). 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 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': 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("MarcoAland/Indo-bge-m3")
# Run inference
sentences = [
    'Orang-orang ada di luar.',
    'Orang-orang berjalan di luar dan mengenakan warna gelap.',
    'Sekelompok orang sedang makan di sebuah restoran dengan mural seorang wanita sedang berbelanja di belakang mereka.',
]
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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## Evaluation

### Metrics

#### Triplet
* Dataset: `model-evaluation`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric             | Value      |
|:-------------------|:-----------|
| cosine_accuracy    | 0.9636     |
| dot_accuracy       | 0.0364     |
| manhattan_accuracy | 0.9625     |
| euclidean_accuracy | 0.9636     |
| **max_accuracy**   | **0.9636** |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 39,836 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                           | positive                                                                          | negative                                                                          |
  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                            | string                                                                            |
  | details | <ul><li>min: 5 tokens</li><li>mean: 9.44 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.41 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.66 tokens</li><li>max: 52 tokens</li></ul> |
* Samples:
  | anchor                                     | positive                                                                           | negative                                                                                    |
  |:-------------------------------------------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------|
  | <code>Seseorang sedang tidur.</code>       | <code>Seorang pemuda tidur siang di jendela sebuah bisnis di pinggir jalan.</code> | <code>Seseorang duduk di kursi yang digantung dengan rantai di taman hiburan.</code>        |
  | <code>Seekor anjing sedang berlari.</code> | <code>Seekor anjing abu-abu berlari di sepanjang rumput hijau.</code>              | <code>Seekor anjing coklat sedang menatap anjing coklat dan putih yang sedang tidur.</code> |
  | <code>Seorang bayi menangis.</code>        | <code>Seorang bayi menangis di tempat tidur bayi.</code>                           | <code>Seorang bayi berbaring telentang dan tersenyum.</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"
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 4,427 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                           | positive                                                                          | negative                                                                          |
  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                            | string                                                                            |
  | details | <ul><li>min: 6 tokens</li><li>mean: 9.41 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.97 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.64 tokens</li><li>max: 42 tokens</li></ul> |
* Samples:
  | anchor                                         | positive                                                         | negative                                                                      |
  |:-----------------------------------------------|:-----------------------------------------------------------------|:------------------------------------------------------------------------------|
  | <code>Seorang pria sedang tidur.</code>        | <code>Seorang pria tidur di rumput di taman.</code>              | <code>Seorang pria membaca koran di samping mobil.</code>                     |
  | <code>Seorang pria sedang membaca buku.</code> | <code>Seorang pria tua duduk di luar sambil membaca buku.</code> | <code>Seorang pria berbaju pelangi berhenti untuk melihat kotak surat.</code> |
  | <code>Anjing coklat melangkah di air.</code>   | <code>Anjing coklat berjalan di air saat dia basah kuyup</code>  | <code>Anjing coklat sedang tidur di samping air</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`: 4
- `per_device_eval_batch_size`: 4
- `max_steps`: 500
- `warmup_ratio`: 0.1
- `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`: 4
- `per_device_eval_batch_size`: 4
- `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`: 1.0
- `num_train_epochs`: 3.0
- `max_steps`: 500
- `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`: 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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | loss   | model-evaluation_max_accuracy |
|:------:|:----:|:-------------:|:------:|:-----------------------------:|
| 0.0100 | 100  | 0.7797        | 0.6925 | -                             |
| 0.0201 | 200  | 0.6337        | 0.6018 | -                             |
| 0.0301 | 300  | 0.6129        | 0.5737 | -                             |
| 0.0402 | 400  | 0.5982        | 0.5116 | -                             |
| 0.0502 | 500  | 0.5504        | 0.4719 | 0.9636                        |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.3.1+cu121
- Accelerate: 0.32.1
- Datasets: 2.20.0
- 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",
}
```

#### 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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