---
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
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:
      config: ar-ar
      name: MTEB STS17 (ar-ar)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: pearson_cosine
      value: 0.8515496450525244
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8558624740720275
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.821963706969713
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8396900657477299
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8231208177674895
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8444168331737782
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8515496381581389
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8557531503465841
      name: Spearman Dot
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      config: en-ar
      name: MTEB STS17 (en-ar)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: pearson_cosine
      value: 0.4960250395119053
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.4770240652715316
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.463401831917928
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.4468968000990917
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.4481739880481376
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.428311112429714
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.49602504450181617
      name: Pearson Dot
    - type: spearman_dot
      value: 0.4770240652715316
      name: Spearman Dot
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: `MTEB STS17 (ar-ar)` [source](https://huggingface.co/datasets/mteb/sts17-crosslingual-sts/viewer/ar-ar)
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8515     |
| **spearman_cosine** | **0.8559** |
| pearson_manhattan   | 0.8220     |
| spearman_manhattan  | 0.8397     |
| pearson_euclidean   | 0.8231     |
| spearman_euclidean  | 0.8444     |
| pearson_dot         | 0.8515     |
| spearman_dot        | 0.8557     |

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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 were 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

**[training script](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 `2` 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

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


</details>

### 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

#### Sentence Transformers Citation
```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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