yasserrmd / emirati-arabic-gemma-300m-emb

SentenceTransformer based on google/embeddinggemma-300m

This is a sentence-transformers model finetuned from google/embeddinggemma-300m . 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: google/embeddinggemma-300m
• Maximum Sequence Length: 2048 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': 2048, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'})
  (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): Dense({'in_features': 768, 'out_features': 3072, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
  (3): Dense({'in_features': 3072, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
  (4): 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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("yasserrmd/emirati-arabic-gemma-300m-emb")
# Run inference
queries = [
    "\u0628\u0643\u0645 \u062a\u0646\u0638\u064a\u0641 \u0627\u0644\u0623\u0630\u0646\u061f",
]
documents = [
    '٥٠ درهم.',
    'نزلته أمس بالليل.',
    'الحمدلله كلهم زينين، يسلمون عليك.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.1448, 0.2254, 0.3522]])

Training Details

Training Dataset

Unnamed Dataset

• Size: 12,324 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: 4 tokens mean: 11.73 tokens max: 64 tokens	min: 4 tokens mean: 14.47 tokens max: 68 tokens

• Samples:

  sentence_0	sentence_1
  كم عمرك؟	٢٧ سنة.
  ما تقدر تنزل أكثر؟	لا والله، ما بقى ربح.
  الجولة البحرية فيها وجبة؟	نعم، عشاء مفتوح.

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size : 6
• per_device_eval_batch_size : 6
• num_train_epochs : 4
• multi_dataset_batch_sampler : round_robin

All Hyperparameters

Click to expand

• overwrite_output_dir : False
• do_predict : False
• eval_strategy : no
• prediction_loss_only : True
• per_device_train_batch_size : 6
• per_device_eval_batch_size : 6
• 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 : 5e-05
• weight_decay : 0.0
• adam_beta1 : 0.9
• adam_beta2 : 0.999
• adam_epsilon : 1e-08
• max_grad_norm : 1
• num_train_epochs : 4
• max_steps : -1
• lr_scheduler_type : linear
• lr_scheduler_kwargs : {}
• warmup_ratio : 0.0
• 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 : False
• 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}
• parallelism_config : None
• deepspeed : None
• label_smoothing_factor : 0.0
• optim : adamw_torch_fused
• 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
• hub_revision : None
• 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
• 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
• liger_kernel_config : None
• eval_use_gather_object : False
• average_tokens_across_devices : False
• prompts : None
• batch_sampler : batch_sampler
• multi_dataset_batch_sampler : round_robin
• router_mapping : {}
• learning_rate_mapping : {}

Training Logs

Got it ✅ Since you tested more than 200 pairs , you can make your README section stronger by showing scale + coverage . Here’s an upgraded version you can paste directly:

---

Evaluation & Benchmark

The model was evaluated on 200+ Emirati Arabic conversational sentence pairs covering greetings, family, culture, food, weather, technology, education, and more.

Strengths

• Greetings & Social Talk → High similarity ( 0.78–0.89 ) for common greetings and check-ins.
• Family & Daily Life → Strong clustering ( 0.7–0.88 ) for expressions about relatives and routine activities.
• Food & Culture → Accurate embeddings for traditional dishes and cultural references ( 0.8–0.95 ).
• Weather & Environment → Excellent handling of synonyms like “الجو حار” ↔ “الطقس حر” ( 0.93+ ).
• Sports Commentary → Captures natural paraphrases ( “اللاعب سجل هدف” ↔ “اللاعب جاب جول” → 0.88 ).
• Tech & Code-switching → Handles Arabic-English mix well ( “Laptop ما يشتغل” ↔ “اللابتوب خربان” ).

Weaknesses

• Negation & Polarity → Sometimes overestimates similarity between opposites ( “بعيد ↔ قريب” ).
• Religious / Abstract Phrases → Inconsistent for Eid, Ramadan, and Quran-related expressions.
• Subtle Emotions → Good with strong polarity ( “غضبان ↔ معصب” ), weaker on softer ones ( “فرحان ↔ سعيد” ).
• Health/Medical Contexts → Direct matches are fine ( “عملية ↔ جراحة” ), indirect links less consistent.

Takeaway

Overall, the model shows robust performance on everyday Emirati Arabic dialogue with high reliability on paraphrases and cultural expressions , while edge cases like negation, abstract phrasing, and subtle emotional tone need refinement.

Framework Versions

• Python: 3.12.11
• Sentence Transformers: 5.1.0
• Transformers: 4.56.1
• PyTorch: 2.8.0+cu128
• Accelerate: 1.10.1
• Datasets: 4.0.0
• Tokenizers: 0.22.0

Citation

BibTeX

Sentence Transformers

@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

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}