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AlgerianME5

Name: algerianME5
Availability: InStock

algerianME5 is a specialized Sentence-Transformer model designed to map Algerian search queries to a 768-dimensional dense vector space, It is specifically fine-tuned to understand the nuances and the vocabulary of the Algerian car and real estate markets, where listings often mix Arabic, French, and darja in both Arabic and Latin script

Note: For more details about the methodology, data synthesis, and evaluation, please visit the full Medium Story

Key Features :

-Domain Specific: Optimized for real estate and automotive algerian vocabulary “sbigha,” “f3,” “livret foncier”

-Cross lingual Retrieval: Maps informal latin queries "tonobil mliha" to formal Arabic or French listing descriptions

-Robust Embeddings: Based on the powerful intfloat/multilingual-e5-base architecture

Use cases :

-Semantic Search : Find relevant listings even if keywords dont match exactly (use it as a second layer)

-Textual Similarity:Compare two listings to find duplicates or similar items

-Clustering Group listings by sub-market or vehicle/property type

Model Details

Model Description

Model Type: Sentence Transformer
Base model: intfloat/multilingual-e5-base
Maximum Sequence Length: 256 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': '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:

pip install -U sentence-transformers

Then you can load this model and run inference.

python

from sentence_transformers import SentenceTransformer


model = SentenceTransformer("81melody/algerianME5")
sentences = [
    'query: Rani nhawes 3la tonobil Hyundai i10',
    'passage: سيارة Hyundai i10 2014 GLS · بنزين · يدوية · 1.1 · المسافة: 300,000 كم · عين تموشنت · Fiha bantoura',
    'passage: سيارة Kia Cerato 2008 · مازوت · يدوية · المسافة: 230,000 كم · السعر: 135 مليون دج · سوق اهراس · Problem də terage',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)

python

from sentence_transformers import SentenceTransformer , util
model = SentenceTransformer("81melody/algerianME5")
listings = [
    # REAL ESTATE
    "بيع شقة 4 غرف الجزائر شراقة · شقة · 4 غرف · السعر: 4 مليون دج · Appartement Composé De 1 Suite Parentale... Résidence sécurisée",
    "كراء شقة 4 غرف وهران وهران · شقة · 4 غرف · Location appartement par jour pour familles",
    "بيع ارض تلمسان مغنية · ارض · الجزائر · بلان فالسانك مليح",
    "كراء محل الجزائر الابيار · محل تجاري · 105 م² · Local avec Deux rideaux",
    
    # CARS
    "سيارة MG Zs Ev 2024 Comfort · بنزين · يدوية · 1.5 VTi-Tech 106ch · المسافة: 67,000 كم · Très beau SUV comme neuf",
    "سيارة Hyundai Grand i10 2018 Restylée DZ · بنزين · يدوية · 1.2 ess 87ch · السعر: 265 مليون دج · صبيغة فيها لال و لامان",
    "سيارة Renault Clio 4 2018 GT Line + · مازوت · يدوية · 1.5 DCI 85ch · السعر: 330 مليون دج"
]
queries = [
    "شقة 4 غرف الجزائر",              
    "dar lel bi3 fi Alger centre",    
    "ard lel bi3 telemcan"   ,           
    "chhal souma MG Zs Ev",          
    "Grand I10 2018 Restylée DZ",     
    "tonobil mliha fiha sbigha shwia" 
]
  q_prefix = "query: "
  p_prefix = "passage: "

  encoded_listings = model.encode(
      [f"{p_prefix}{l}" for l in listings], 
      convert_to_tensor=True,
      show_progress_bar=False
  )
  for query in queries:
      print(f"\nQuery: '{query}'")
      
      
      query_emb = model.encode(f"{q_prefix}{query}", convert_to_tensor=True)
      hits = util.semantic_search(query_emb, encoded_listings, top_k=3)[0]


      for i, hit in enumerate(hits):
          score = hit['score']
          doc_id = hit['corpus_id']
          display_text = listings[doc_id][:100] + "..." if len(listings[doc_id]) > 100 else listings[doc_id]
          print(f"[Score: {score:.3f}] {display_text}")

Training Details

Training Dataset

Size: 100,000 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1
type string string
details
min: 7 tokens
mean: 11.07 tokens
max: 22 tokens
min: 17 tokens
mean: 82.2 tokens
max: 256 tokens

Samples:

sentence_0	sentence_1
`query: بيع محل وهران بئر`	`passage: بيع محل وهران بئر الجير · محل تجاري · 750 م² · السعر: 20 مليار دج · وهران · On vous propose en vente un local de 750 m² (550 m² en rez-de-chaussée et 200 m² sous pente) , avec deux rideaux électriques , pour le prix de : 20 Milliards fixe .[object Object][object Object]Pour plus de détails veuillez nous contacter`
`query: شقة الجزائر برج`	passage: بيع شقة الجزائر برج الكيفان · شقة · 1 غرف · 64 م² · وثائق: دفتر عقاري · عقد موثق · الجزائر · 🔔OPPORTUNITÉ EN OR 🔔[object Object]– T2 à vendre +paiement par tranche dans 24mois[object Object][object Object]❄️À seulement quelques pas de la piscine, dans une site sécurisée et bien située, ce T2 en semi-finis une valeur sûre pour tout investisseur avisé.[object Object][object Object]Pourquoi ce bien est exceptionnel ?[object Object]✅️Localisation stratégique, très demandée[object Object]✅️Retour sur investissement rapide[object Object]✅️Prêt à être exploité dès l’achat ![object Object]✅️Un petit prix pour un grand potentiel.[object Object]✅️Les bonnes affaires ne durent jamais longtemps…[object Object]Saisissez cette opportunité maintenant !
`query: GX3 PRO 2025 X3 Pro`	`passage: سيارة Geely GX3 PRO 2025 X3 pro livane · بنزين · اوتوماتيك · 1.5 · المسافة: جديدة · بجاية · Vent une livane x3pro neuf carte grise Safia`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false, 5 "directions": [ 6 "query_to_doc" 7 ], 8 "partition_mode": "joint", 9 "hardness_mode": null, 10 "hardness_strength": 0.0 11}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
per_device_eval_batch_size: 16
fp16: True
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss
...	...	...
2.32	14500	0.2827
2.4	15000	0.3062
2.48	15500	0.3045
2.56	16000	0.2841

Framework Versions

Python: 3.12.13
Sentence Transformers: 5.3.0
Transformers: 5.0.0
PyTorch: 2.10.0+cu128
Accelerate: 1.13.0
Datasets: 4.0.0
Tokenizers: 0.22.2

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{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}

Contact

Iam interested in any further related work, contact me at mohamed.himeur@student.unamur.be

	sentence_0	sentence_1
type	string	string
details	min: 7 tokens mean: 11.07 tokens max: 22 tokens	min: 17 tokens mean: 82.2 tokens max: 256 tokens