README.md

---
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
- mteb
model-index:
- name: silma-ai/silma-embeddding-sts-0.1
  results:
  - dataset:
      config: ar
      name: MTEB MassiveIntentClassification (ar)
      revision: 4672e20407010da34463acc759c162ca9734bca6
      split: test
      type: mteb/amazon_massive_intent
    metrics:
    - type: accuracy
      value: 56.489576328177534
    - type: f1
      value: 54.0532701115665
    - type: f1_weighted
      value: 56.74231335142343
    - type: main_score
      value: 56.489576328177534
    task:
      type: Classification
  - dataset:
      config: en
      name: MTEB MassiveIntentClassification (en)
      revision: 4672e20407010da34463acc759c162ca9734bca6
      split: test
      type: mteb/amazon_massive_intent
    metrics:
    - type: accuracy
      value: 48.78278412911903
    - type: f1
      value: 47.56043284146044
    - type: f1_weighted
      value: 48.98016672316552
    - type: main_score
      value: 48.78278412911903
    task:
      type: Classification
  - dataset:
      config: ar
      name: MTEB MassiveIntentClassification (ar)
      revision: 4672e20407010da34463acc759c162ca9734bca6
      split: validation
      type: mteb/amazon_massive_intent
    metrics:
    - type: accuracy
      value: 56.768322675848495
    - type: f1
      value: 53.963930379828895
    - type: f1_weighted
      value: 56.745501043116796
    - type: main_score
      value: 56.768322675848495
    task:
      type: Classification
  - dataset:
      config: en
      name: MTEB MassiveIntentClassification (en)
      revision: 4672e20407010da34463acc759c162ca9734bca6
      split: validation
      type: mteb/amazon_massive_intent
    metrics:
    - type: accuracy
      value: 49.54254795868175
    - type: f1
      value: 48.048926632026195
    - type: f1_weighted
      value: 49.60112881916927
    - type: main_score
      value: 49.54254795868175
    task:
      type: Classification
  - dataset:
      config: ar
      name: MTEB MassiveScenarioClassification (ar)
      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
      split: test
      type: mteb/amazon_massive_scenario
    metrics:
    - type: accuracy
      value: 62.76395427034298
    - type: f1
      value: 62.795517645393474
    - type: f1_weighted
      value: 61.993985553919295
    - type: main_score
      value: 62.76395427034298
    task:
      type: Classification
  - dataset:
      config: en
      name: MTEB MassiveScenarioClassification (en)
      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
      split: test
      type: mteb/amazon_massive_scenario
    metrics:
    - type: accuracy
      value: 55.457296570275716
    - type: f1
      value: 53.04898507492993
    - type: f1_weighted
      value: 55.69280690585543
    - type: main_score
      value: 55.457296570275716
    task:
      type: Classification
  - dataset:
      config: ar
      name: MTEB MassiveScenarioClassification (ar)
      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
      split: validation
      type: mteb/amazon_massive_scenario
    metrics:
    - type: accuracy
      value: 61.76586325627152
    - type: f1
      value: 62.096444561700956
    - type: f1_weighted
      value: 61.253818773337635
    - type: main_score
      value: 61.76586325627152
    task:
      type: Classification
  - dataset:
      config: en
      name: MTEB MassiveScenarioClassification (en)
      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
      split: validation
      type: mteb/amazon_massive_scenario
    metrics:
    - type: accuracy
      value: 55.248401377274966
    - type: f1
      value: 53.5659818815448
    - type: f1_weighted
      value: 55.392941321965914
    - type: main_score
      value: 55.248401377274966
    task:
      type: Classification
  - dataset:
      config: en-ar
      name: MTEB STS17 (en-ar)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: cosine_pearson
      value: 49.60250026530193
    - type: cosine_spearman
      value: 47.702406527153165
    - type: euclidean_pearson
      value: 44.81740010078862
    - type: euclidean_spearman
      value: 42.831111242971396
    - type: main_score
      value: 47.702406527153165
    - type: manhattan_pearson
      value: 46.340186748112124
    - type: manhattan_spearman
      value: 44.689680009909175
    - type: pearson
      value: 49.60250612700404
    - type: spearman
      value: 47.702406527153165
    task:
      type: STS
  - dataset:
      config: en-en
      name: MTEB STS17 (en-en)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: cosine_pearson
      value: 80.50355999312305
    - type: cosine_spearman
      value: 80.05684742492551
    - type: euclidean_pearson
      value: 79.79426226586054
    - type: euclidean_spearman
      value: 78.62531622907113
    - type: main_score
      value: 80.05684742492551
    - type: manhattan_pearson
      value: 79.69928765568616
    - type: manhattan_spearman
      value: 78.57030908261245
    - type: pearson
      value: 80.50356022284683
    - type: spearman
      value: 80.05684742492551
    task:
      type: STS
  - dataset:
      config: es-en
      name: MTEB STS17 (es-en)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: cosine_pearson
      value: 21.624383947189354
    - type: cosine_spearman
      value: 21.4038834628452
    - type: euclidean_pearson
      value: 7.184950714569936
    - type: euclidean_spearman
      value: 3.4762228403044304
    - type: main_score
      value: 21.4038834628452
    - type: manhattan_pearson
      value: 6.551289317075073
    - type: manhattan_spearman
      value: 2.286368561838714
    - type: pearson
      value: 21.624390367032202
    - type: spearman
      value: 21.4038834628452
    task:
      type: STS
  - dataset:
      config: en-de
      name: MTEB STS17 (en-de)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: cosine_pearson
      value: 31.03301067892329
    - type: cosine_spearman
      value: 31.85713324783654
    - type: euclidean_pearson
      value: 21.63310145118274
    - type: euclidean_spearman
      value: 22.456677151668814
    - type: main_score
      value: 31.85713324783654
    - type: manhattan_pearson
      value: 21.67370664986112
    - type: manhattan_spearman
      value: 21.598819368637155
    - type: pearson
      value: 31.03301931810337
    - type: spearman
      value: 31.85713324783654
    task:
      type: STS
  - dataset:
      config: fr-en
      name: MTEB STS17 (fr-en)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: cosine_pearson
      value: 30.07580974074585
    - type: cosine_spearman
      value: 30.070765595685838
    - type: euclidean_pearson
      value: 17.235942672907232
    - type: euclidean_spearman
      value: 16.010962024640964
    - type: main_score
      value: 30.070765595685838
    - type: manhattan_pearson
      value: 16.98929367890981
    - type: manhattan_spearman
      value: 15.865314171439055
    - type: pearson
      value: 30.075805759312956
    - type: spearman
      value: 30.070765595685838
    task:
      type: STS
  - dataset:
      config: nl-en
      name: MTEB STS17 (nl-en)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: cosine_pearson
      value: 38.5738832598024
    - type: cosine_spearman
      value: 36.23552528353376
    - type: euclidean_pearson
      value: 28.920909050416814
    - type: euclidean_spearman
      value: 26.824767359797256
    - type: main_score
      value: 36.23552528353376
    - type: manhattan_pearson
      value: 28.449235903219787
    - type: manhattan_spearman
      value: 26.149497938525712
    - type: pearson
      value: 38.57388759602166
    - type: spearman
      value: 36.23552528353376
    task:
      type: STS
  - dataset:
      config: it-en
      name: MTEB STS17 (it-en)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: cosine_pearson
      value: 28.440771017135734
    - type: cosine_spearman
      value: 23.328373210539134
    - type: euclidean_pearson
      value: 14.616541134326836
    - type: euclidean_spearman
      value: 7.785452426485771
    - type: main_score
      value: 23.328373210539134
    - type: manhattan_pearson
      value: 16.35791121049381
    - type: manhattan_spearman
      value: 10.350376853181583
    - type: pearson
      value: 28.440782342934394
    - type: spearman
      value: 23.328373210539134
    task:
      type: STS
  - dataset:
      config: en-tr
      name: MTEB STS17 (en-tr)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: cosine_pearson
      value: 10.058384831429683
    - type: cosine_spearman
      value: 9.208230020320498
    - type: euclidean_pearson
      value: -3.778073300045484
    - type: euclidean_spearman
      value: -5.168172155878574
    - type: main_score
      value: 9.208230020320498
    - type: manhattan_pearson
      value: -5.081387114365387
    - type: manhattan_spearman
      value: -5.190932828652431
    - type: pearson
      value: 10.058387061356784
    - type: spearman
      value: 9.208230020320498
    task:
      type: STS
  - dataset:
      config: ar-ar
      name: MTEB STS17 (ar-ar)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: cosine_pearson
      value: 85.15496368852482
    - type: cosine_spearman
      value: 85.58624740720275
    - type: euclidean_pearson
      value: 82.31207769687893
    - type: euclidean_spearman
      value: 84.44298391864797
    - type: main_score
      value: 85.58624740720275
    - type: manhattan_pearson
      value: 82.19636675129995
    - type: manhattan_spearman
      value: 83.97030581469602
    - type: pearson
      value: 85.15496353205859
    - type: spearman
      value: 85.59382070976062
    task:
      type: STS
  - dataset:
      config: es-en
      name: MTEB STS22.v2 (es-en)
      revision: d31f33a128469b20e357535c39b82fb3c3f6f2bd
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
    - type: cosine_pearson
      value: 44.24743366469854
    - type: cosine_spearman
      value: 50.28917533427211
    - type: euclidean_pearson
      value: 45.87986269990654
    - type: euclidean_spearman
      value: 51.891514435608855
    - type: main_score
      value: 50.28917533427211
    - type: manhattan_pearson
      value: 45.45542397032592
    - type: manhattan_spearman
      value: 52.411033818833666
    - type: pearson
      value: 44.24743853113377
    - type: spearman
      value: 50.28917533427211
    task:
      type: STS
  - dataset:
      config: zh-en
      name: MTEB STS22.v2 (zh-en)
      revision: d31f33a128469b20e357535c39b82fb3c3f6f2bd
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
    - type: cosine_pearson
      value: 27.73878924884296
    - type: cosine_spearman
      value: 22.44663617360493
    - type: euclidean_pearson
      value: 22.868571735387977
    - type: euclidean_spearman
      value: 18.017657427593637
    - type: main_score
      value: 22.44663617360493
    - type: manhattan_pearson
      value: 24.20368152236799
    - type: manhattan_spearman
      value: 19.341058710109657
    - type: pearson
      value: 27.738791387167687
    - type: spearman
      value: 22.44663617360493
    task:
      type: STS
  - dataset:
      config: de-en
      name: MTEB STS22.v2 (de-en)
      revision: d31f33a128469b20e357535c39b82fb3c3f6f2bd
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
    - type: cosine_pearson
      value: 28.905819837460527
    - type: cosine_spearman
      value: 32.52679512081778
    - type: euclidean_pearson
      value: 28.61574417382465
    - type: euclidean_spearman
      value: 35.447663167023094
    - type: main_score
      value: 32.52679512081778
    - type: manhattan_pearson
      value: 28.736369410178426
    - type: manhattan_spearman
      value: 35.158643077723944
    - type: pearson
      value: 28.90580871894244
    - type: spearman
      value: 32.52679512081778
    task:
      type: STS
  - dataset:
      config: pl-en
      name: MTEB STS22.v2 (pl-en)
      revision: d31f33a128469b20e357535c39b82fb3c3f6f2bd
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
    - type: cosine_pearson
      value: 48.20842591896265
    - type: cosine_spearman
      value: 44.838254673346626
    - type: euclidean_pearson
      value: 51.55940058938421
    - type: euclidean_spearman
      value: 45.912821863788785
    - type: main_score
      value: 44.838254673346626
    - type: manhattan_pearson
      value: 52.13078297712538
    - type: manhattan_spearman
      value: 47.402814514453425
    - type: pearson
      value: 48.20843799095813
    - type: spearman
      value: 44.838254673346626
    task:
      type: STS
  - dataset:
      config: en
      name: MTEB STS22.v2 (en)
      revision: d31f33a128469b20e357535c39b82fb3c3f6f2bd
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
    - type: cosine_pearson
      value: 56.896647953120414
    - type: cosine_spearman
      value: 60.96741836410487
    - type: euclidean_pearson
      value: 55.90453382184861
    - type: euclidean_spearman
      value: 60.273680095845705
    - type: main_score
      value: 60.96741836410487
    - type: manhattan_pearson
      value: 55.87830113983942
    - type: manhattan_spearman
      value: 59.94276270978964
    - type: pearson
      value: 56.89664991046338
    - type: spearman
      value: 60.96741836410487
    task:
      type: STS
  - dataset:
      config: ar
      name: MTEB STS22.v2 (ar)
      revision: d31f33a128469b20e357535c39b82fb3c3f6f2bd
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
    - type: cosine_pearson
      value: 52.70294726367241
    - type: cosine_spearman
      value: 61.21881191987154
    - type: euclidean_pearson
      value: 54.13531251250594
    - type: euclidean_spearman
      value: 61.20287919055926
    - type: main_score
      value: 61.21881191987154
    - type: manhattan_pearson
      value: 54.60474684752885
    - type: manhattan_spearman
      value: 61.45150178016683
    - type: pearson
      value: 52.70294625001791
    - type: spearman
      value: 61.21881191987154
    task:
      type: STS
license: apache-2.0
language:
- ar
- en
---

# SILMA STS Arabic Embedding Model 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

### Citation:

#### BibTeX:

```bibtex
@misc{silma2024embedding,
  author = {Abu Bakr Soliman, Karim Ouda, SILMA AI},
  title = {SILMA Embedding STS 0.1},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/silma-ai/silma-embeddding-sts-0.1}},
}
```

#### APA:

```apa
Abu Bakr Soliman, Karim Ouda, SILMA AI. (2024). SILMA Embedding STS 0.1 [Model]. Hugging Face. https://huggingface.co/silma-ai/silma-embeddding-sts-0.1
```

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