---
base_model: silma-ai/silma-embeddding-matryoshka-0.1
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
- loss:CosineSimilarityLoss
model-index:
- name: SentenceTransformer based on silma-ai/silma-embeddding-matryoshka-0.1
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 512
      type: sts-dev-512
    metrics:
    - type: pearson_cosine
      value: 0.8509127994264242
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8548500966032416
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.821303728669975
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8364598068079891
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8210450198328316
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8382181658285147
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8491261828772604
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8559811107036664
      name: Spearman Dot
    - type: pearson_max
      value: 0.8509127994264242
      name: Pearson Max
    - type: spearman_max
      value: 0.8559811107036664
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 256
      type: sts-dev-256
    metrics:
    - type: pearson_cosine
      value: 0.8498025312190702
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8530609768738506
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8181745876468085
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8328727236454085
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8193792688284338
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8338632184708783
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8396368156921546
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8484397673758116
      name: Spearman Dot
    - type: pearson_max
      value: 0.8498025312190702
      name: Pearson Max
    - type: spearman_max
      value: 0.8530609768738506
      name: Spearman Max
license: apache-2.0
language:
- ar
- en
---

# SentenceTransformer based on silma-ai/silma-embeddding-matryoshka-0.1

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [silma-ai/silma-embeddding-matryoshka-0.1](https://huggingface.co/silma-ai/silma-embeddding-matryoshka-0.1). 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:** [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then load the model

```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim

model = SentenceTransformer("silma-ai/silma-embeddding-sts-0.1")
```

### Samples

#### [+] Short Sentence Similarity

**Arabic**
```python
query = "الطقس اليوم مشمس"
sentence_1 = "الجو اليوم كان مشمسًا ورائعًا"
sentence_2 = "الطقس اليوم غائم"

query_embedding = model.encode(query)

print("sentence_1_similarity:", cos_sim(query_embedding, model.encode(sentence_1))[0][0].tolist())
print("sentence_2_similarity:", cos_sim(query_embedding, model.encode(sentence_2))[0][0].tolist())

# ======= Output
# sentence_1_similarity: 0.42602288722991943
# sentence_2_similarity: 0.10798501968383789
# =======
```

**English**
```python
query = "The weather is sunny today"
sentence_1 = "The morning was bright and sunny"
sentence_2 = "it is too cloudy today"

query_embedding = model.encode(query)

print("sentence_1_similarity:", cos_sim(query_embedding, model.encode(sentence_1))[0][0].tolist())
print("sentence_2_similarity:", cos_sim(query_embedding, model.encode(sentence_2))[0][0].tolist())

# ======= Output
# sentence_1_similarity: 0.5796191692352295
# sentence_2_similarity: 0.21948376297950745
# =======
```

#### [+] Long Sentence Similarity

**Arabic**
```python
query = "الكتاب يتحدث عن أهمية الذكاء الاصطناعي في تطوير المجتمعات الحديثة"
sentence_1 = "في هذا الكتاب، يناقش الكاتب كيف يمكن للتكنولوجيا أن تغير العالم"
sentence_2 = "الكاتب يتحدث عن أساليب الطبخ التقليدية في دول البحر الأبيض المتوسط"

query_embedding = model.encode(query)

print("sentence_1_similarity:", cos_sim(query_embedding, model.encode(sentence_1))[0][0].tolist())
print("sentence_2_similarity:", cos_sim(query_embedding, model.encode(sentence_2))[0][0].tolist())

# ======= Output
# sentence_1_similarity: 0.5725120306015015
# sentence_2_similarity: 0.22617210447788239
# =======
```

**English**
```python
query = "China said on Saturday it would issue special bonds to help its sputtering economy, signalling a spending spree to bolster banks"
sentence_1 = "The Chinese government announced plans to release special bonds aimed at supporting its struggling economy and stabilizing the banking sector."
sentence_2 = "Several countries are preparing for a global technology summit to discuss advancements in bolster global banks."

query_embedding = model.encode(query)

print("sentence_1_similarity:", cos_sim(query_embedding, model.encode(sentence_1))[0][0].tolist())
print("sentence_2_similarity:", cos_sim(query_embedding, model.encode(sentence_2))[0][0].tolist())

# ======= Output
# sentence_1_similarity: 0.6438770294189453
# sentence_2_similarity: 0.4720292389392853
# =======
```

#### [+] Question to Paragraph Matching

**Arabic**
```python
query = "ما هي فوائد ممارسة الرياضة؟"
sentence_1 = "ممارسة الرياضة بشكل منتظم تساعد على تحسين الصحة العامة واللياقة البدنية"
sentence_2 = "تعليم الأطفال في سن مبكرة يساعدهم على تطوير المهارات العقلية بسرعة"

query_embedding = model.encode(query)

print("sentence_1_similarity:", cos_sim(query_embedding, model.encode(sentence_1))[0][0].tolist())
print("sentence_2_similarity:", cos_sim(query_embedding, model.encode(sentence_2))[0][0].tolist())

# ======= Output
# sentence_1_similarity: 0.6058318614959717
# sentence_2_similarity: 0.006831036880612373
# =======
```

**English**
```python
query = "What are the benefits of exercising?"
sentence_1 = "Regular exercise helps improve overall health and physical fitness"
sentence_2 = "Teaching children at an early age helps them develop cognitive skills quickly"

query_embedding = model.encode(query)

print("sentence_1_similarity:", cos_sim(query_embedding, model.encode(sentence_1))[0][0].tolist())
print("sentence_2_similarity:", cos_sim(query_embedding, model.encode(sentence_2))[0][0].tolist())

# ======= Output
# sentence_1_similarity: 0.3593001365661621
# sentence_2_similarity: 0.06493218243122101
# =======
```

#### [+] Message to Intent-Name Mapping

**Arabic**
```python
query = "أرغب في حجز تذكرة طيران من دبي الى القاهرة يوم الثلاثاء القادم"
sentence_1 = "حجز رحلة"
sentence_2 = "إلغاء حجز"

query_embedding = model.encode(query)

print("sentence_1_similarity:", cos_sim(query_embedding, model.encode(sentence_1))[0][0].tolist())
print("sentence_2_similarity:", cos_sim(query_embedding, model.encode(sentence_2))[0][0].tolist())

# ======= Output
# sentence_1_similarity: 0.4646468162536621
# sentence_2_similarity: 0.19563665986061096
# =======
```

**English**
```python
query = "Please send an email to all of the managers"
sentence_1 = "send email"
sentence_2 = "read inbox emails"

query_embedding = model.encode(query)

print("sentence_1_similarity:", cos_sim(query_embedding, model.encode(sentence_1))[0][0].tolist())
print("sentence_2_similarity:", cos_sim(query_embedding, model.encode(sentence_2))[0][0].tolist())

# ======= Output
# sentence_1_similarity: 0.6485046744346619
# sentence_2_similarity: 0.43906497955322266
# =======

```

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## Evaluation

### Metrics

#### Semantic Similarity
* Dataset: `sts-dev-512`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8509     |
| **spearman_cosine** | **0.8549** |
| pearson_manhattan   | 0.8213     |
| spearman_manhattan  | 0.8365     |
| pearson_euclidean   | 0.821      |
| spearman_euclidean  | 0.8382     |
| pearson_dot         | 0.8491     |
| spearman_dot        | 0.856      |
| pearson_max         | 0.8509     |
| spearman_max        | 0.856      |

#### Semantic Similarity
* Dataset: `sts-dev-256`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8498     |
| **spearman_cosine** | **0.8531** |
| pearson_manhattan   | 0.8182     |
| spearman_manhattan  | 0.8329     |
| pearson_euclidean   | 0.8194     |
| spearman_euclidean  | 0.8339     |
| pearson_dot         | 0.8396     |
| spearman_dot        | 0.8484     |
| pearson_max         | 0.8498     |
| spearman_max        | 0.8531     |

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## Training Details

This model was fine-tuned via 2 phases:

### Phase 1:

In phase `1`, we curated a dataset [silma-ai/silma-arabic-triplets-dataset-v1.0](https://huggingface.co/datasets/silma-ai/silma-arabic-triplets-dataset-v1.0) which
contains more than `2.25M` records of (anchor, positive and negative) Arabic/English samples. 
Only the first `600` samples were taken to be the `eval` dataset, while the rest was used for fine-tuning.

Phase `1` produces a finetuned `Matryoshka` model based on [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) with the following hyperparameters:

- `per_device_train_batch_size`: 250
- `per_device_eval_batch_size`: 10
- `learning_rate`: 1e-05
- `num_train_epochs`: 3
- `bf16`: True
- `dataloader_drop_last`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates

**[trainin-example](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/matryoshka/matryoshka_sts.py)**


### Phase 2:

In phase `2`, we curated a dataset [silma-ai/silma-arabic-english-sts-dataset-v1.0](https://huggingface.co/datasets/silma-ai/silma-arabic-english-sts-dataset-v1.0) which
contains more than `30k` records of (sentence1, sentence2 and similarity-score) Arabic/English samples. 
Only the first `100` samples were taken to be the `eval` dataset, while the rest was used for fine-tuning. 

Phase `1` produces a finetuned `STS` model based on the model from phase `1`, with the following hyperparameters:

- `eval_strategy`: steps
- `per_device_train_batch_size`: 250
- `per_device_eval_batch_size`: 10
- `learning_rate`: 1e-06
- `num_train_epochs`: 10
- `bf16`: True
- `dataloader_drop_last`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates

**[trainin-example](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/sts/training_stsbenchmark_continue_training.py)**


</details>

### Training Logs (Phase 2)
| Epoch  | Step | Training Loss | Validation Loss | sts-dev-512_spearman_cosine | sts-dev-256_spearman_cosine |
|:------:|:----:|:-------------:|:---------------:|:---------------------------:|:---------------------------:|
| 0.3650 | 50   | 0.0395        | 0.0424          | 0.8486                      | 0.8487                      |
| 0.7299 | 100  | 0.031         | 0.0427          | 0.8493                      | 0.8495                      |
| 1.0949 | 150  | 0.0344        | 0.0430          | 0.8496                      | 0.8496                      |
| 1.4599 | 200  | 0.0313        | 0.0427          | 0.8506                      | 0.8504                      |
| 1.8248 | 250  | 0.0267        | 0.0428          | 0.8504                      | 0.8506                      |
| 2.1898 | 300  | 0.0309        | 0.0429          | 0.8516                      | 0.8515                      |
| 2.5547 | 350  | 0.0276        | 0.0425          | 0.8531                      | 0.8521                      |
| 2.9197 | 400  | 0.028         | 0.0426          | 0.8530                      | 0.8515                      |
| 3.2847 | 450  | 0.0281        | 0.0425          | 0.8539                      | 0.8521                      |
| 3.6496 | 500  | 0.0248        | 0.0425          | 0.8542                      | 0.8523                      |
| 4.0146 | 550  | 0.0302        | 0.0424          | 0.8541                      | 0.8520                      |
| 4.3796 | 600  | 0.0261        | 0.0421          | 0.8545                      | 0.8523                      |
| 4.7445 | 650  | 0.0233        | 0.0420          | 0.8544                      | 0.8522                      |
| 5.1095 | 700  | 0.0281        | 0.0419          | 0.8547                      | 0.8528                      |
| 5.4745 | 750  | 0.0257        | 0.0419          | 0.8546                      | 0.8531                      |
| 5.8394 | 800  | 0.0235        | 0.0418          | 0.8546                      | 0.8527                      |
| 6.2044 | 850  | 0.0268        | 0.0418          | 0.8551                      | 0.8529                      |
| 6.5693 | 900  | 0.0238        | 0.0416          | 0.8552                      | 0.8526                      |
| 6.9343 | 950  | 0.0255        | 0.0416          | 0.8549                      | 0.8526                      |
| 7.2993 | 1000 | 0.0253        | 0.0416          | 0.8548                      | 0.8528                      |
| 7.6642 | 1050 | 0.0225        | 0.0415          | 0.8550                      | 0.8525                      |
| 8.0292 | 1100 | 0.0276        | 0.0414          | 0.8550                      | 0.8528                      |
| 8.3942 | 1150 | 0.0244        | 0.0415          | 0.8550                      | 0.8533                      |
| 8.7591 | 1200 | 0.0218        | 0.0414          | 0.8551                      | 0.8529                      |
| 9.1241 | 1250 | 0.0263        | 0.0414          | 0.8550                      | 0.8531                      |
| 9.4891 | 1300 | 0.0241        | 0.0414          | 0.8552                      | 0.8533                      |
| 9.8540 | 1350 | 0.0227        | 0.0415          | 0.8549                      | 0.8531                      |


### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.2.0
- Transformers: 4.45.2
- PyTorch: 2.3.1
- Accelerate: 1.0.1
- Datasets: 3.0.1
- Tokenizers: 0.20.1

## 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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