---
language:
- id
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:6198
- loss:CoSENTLoss
base_model: intfloat/multilingual-e5-base
datasets:
- Pustekhan-ITB/stsb-indo-edu
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on intfloat/multilingual-e5-base
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: stsb indo edu dev
      type: stsb-indo-edu-dev
    metrics:
    - type: pearson_cosine
      value: 0.1930033858243812
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.17647076252403324
      name: Spearman Cosine
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: stsb indo edu test
      type: stsb-indo-edu-test
    metrics:
    - type: pearson_cosine
      value: 0.15065000397563194
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.1512326380689479
      name: Spearman Cosine
---

# SentenceTransformer based on intfloat/multilingual-e5-base

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) on the [stsb-indo-edu](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu) 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:** [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) <!-- at revision 835193815a3936a24a0ee7dc9e3d48c1fbb19c55 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [stsb-indo-edu](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu)
- **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: XLMRobertaModel 
  (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})
  (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("ewideplus/indoedu-e5-base")
# Run inference
sentences = [
    'The weather is lovely today.',
    "It's so sunny outside!",
    'He drove to the stadium.',
]
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

* Datasets: `stsb-indo-edu-dev` and `stsb-indo-edu-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | stsb-indo-edu-dev | stsb-indo-edu-test |
|:--------------------|:------------------|:-------------------|
| pearson_cosine      | 0.193             | 0.1507             |
| **spearman_cosine** | **0.1765**        | **0.1512**         |

<!--
## 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

#### stsb-indo-edu

* Dataset: [stsb-indo-edu](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu) at [f84d4d6](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu/tree/f84d4d6eaca768507bd0f298aef6f3f1a98ddefc)
* Size: 6,198 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    | list                                                                                      | list                                                                                      | float                                                          |
  | details | <ul><li>min: 18 elements</li><li>mean: 58.40 elements</li><li>max: 137 elements</li></ul> | <ul><li>min: 15 elements</li><li>mean: 54.31 elements</li><li>max: 118 elements</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.46</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                                      | sentence2                                                                                | score             |
  |:-------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|:------------------|
  | <code>['query: P', 'query: e', 'query: l', 'query: a', 'query: j', ...]</code> | <code>['passage: T', 'passage: a', 'passage: r', 'passage: i', 'passage: a', ...]</code> | <code>0.76</code> |
  | <code>['query: S', 'query: e', 'query: b', 'query: e', 'query: l', ...]</code> | <code>['passage: U', 'passage: p', 'passage: a', 'passage: y', 'passage: a', ...]</code> | <code>0.85</code> |
  | <code>['query: B', 'query: e', 'query: b', 'query: e', 'query: r', ...]</code> | <code>['passage: I', 'passage: n', 'passage: i', 'passage:  ', 'passage: m', ...]</code> | <code>0.63</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```

### Evaluation Dataset

#### stsb-indo-edu

* Dataset: [stsb-indo-edu](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu) at [f84d4d6](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu/tree/f84d4d6eaca768507bd0f298aef6f3f1a98ddefc)
* Size: 1,536 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                                 | sentence2                                                                                 | score                                                          |
  |:--------|:------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | list                                                                                      | list                                                                                      | float                                                          |
  | details | <ul><li>min: 14 elements</li><li>mean: 86.67 elements</li><li>max: 172 elements</li></ul> | <ul><li>min: 22 elements</li><li>mean: 88.94 elements</li><li>max: 177 elements</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                                      | sentence2                                                                                | score             |
  |:-------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|:------------------|
  | <code>['query: S', 'query: e', 'query: o', 'query: r', 'query: a', ...]</code> | <code>['passage: S', 'passage: e', 'passage: o', 'passage: r', 'passage: a', ...]</code> | <code>1.0</code>  |
  | <code>['query: S', 'query: e', 'query: o', 'query: r', 'query: a', ...]</code> | <code>['passage: S', 'passage: e', 'passage: o', 'passage: r', 'passage: a', ...]</code> | <code>0.95</code> |
  | <code>['query: S', 'query: e', 'query: o', 'query: r', 'query: a', ...]</code> | <code>['passage: P', 'passage: r', 'passage: i', 'passage: a', 'passage:  ', ...]</code> | <code>1.0</code>  |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `learning_rate`: 1e-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`: 32
- `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
- `torch_empty_cache_steps`: None
- `learning_rate`: 1e-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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | Validation Loss | stsb-indo-edu-dev_spearman_cosine | stsb-indo-edu-test_spearman_cosine |
|:------:|:----:|:-------------:|:---------------:|:---------------------------------:|:----------------------------------:|
| -1     | -1   | -             | -               | 0.0995                            | -                                  |
| 0.5155 | 100  | 6.2244        | 4.7594          | 0.1027                            | -                                  |
| 1.0309 | 200  | 6.1605        | 4.7518          | 0.1502                            | -                                  |
| 1.5464 | 300  | 6.16          | 4.7553          | 0.1564                            | -                                  |
| 2.0619 | 400  | 6.1609        | 4.7527          | 0.1714                            | -                                  |
| 2.5773 | 500  | 6.1593        | 4.7698          | 0.1495                            | -                                  |
| 3.0928 | 600  | 6.1517        | 4.7516          | 0.1657                            | -                                  |
| 3.6082 | 700  | 6.1555        | 4.7463          | 0.1787                            | -                                  |
| 4.1237 | 800  | 6.1452        | 4.7548          | 0.1665                            | -                                  |
| 4.6392 | 900  | 6.1523        | 4.7494          | 0.1765                            | -                                  |
| -1     | -1   | -             | -               | -                                 | 0.1512                             |


### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0

## 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",
}
```

#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}
```

<!--
## 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.*
-->