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

sourceHugging Faceupdated 1y agoView on Hugging Face

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Model Card

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:

bash

pip install -U sentence-transformers

Then you can load this model and run inference.

python

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]])

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Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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Training Details

Training Dataset

Unnamed Dataset

—Size: 12,324 training samples
—Columns: <code>sentence0</code> and <code>sentence1</code>
—Approximate statistics based on the first 1000 samples: | | sentence0 | sentence1 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 4 tokens</li><li>mean: 11.73 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.47 tokens</li><li>max: 68 tokens</li></ul> |
—Samples: | sentence0 | sentence1 | |:---------------------------------------|:-----------------------------------| | <code>كم عمرك؟</code> | <code>٢٧ سنة.</code> | | <code>ما تقدر تنزل أكثر؟</code> | <code>لا والله، ما بقى ربح.</code> | | <code>الجولة البحرية فيها وجبة؟</code> | <code>نعم، عشاء مفتوح.</code> |
—Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

json

  {
      "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

<details><summary>Click to expand</summary>

—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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
—fsdp_transformer_layer_cls_to_wrap: None
—accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
—parallelism_config: None
—deepspeed: None
—label_smoothing_factor: 0.0
—optim: adamwtorchfused
—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: {}

</details>

Training Logs

Epoch	Step	Training Loss
0.2434	500	1.0578
0.4869	1000	0.7525
0.7303	1500	0.5706
0.9737	2000	0.4128
0.2434	2500	0.4749
0.4869	3000	0.5956
0.7303	3500	0.5322
0.9737	4000	0.476
1.2171	4500	0.3686
1.4606	5000	0.3213
1.7040	5500	0.3192
1.9474	6000	0.2964
2.1908	6500	0.2151
2.4343	7000	0.1891
2.6777	7500	0.1668
2.9211	8000	0.1669
3.1646	8500	0.1
3.4080	9000	0.0948
3.6514	9500	0.1017
3.8948	10000	0.076

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

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{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}
}

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