This is a sentence-transformers model finetuned from intfloat/multilingual-e5-base. 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: intfloat/multilingual-e5-base
Maximum Sequence Length: 512 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': 512, 'do_lower_case': False}) with Transformer model: 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

# Download from the 🤗 Hub
model = SentenceTransformer("AhmedZaky1/arabic-e5-multilingual-finetuned-20250530")
# Run inference
sentences = [
    'كيف يمكنني الترويج لموقعك الإلكتروني؟',
    'ما هي أفضل طريقة للترويج لموقعك الإلكتروني؟',
    'امرأة ترقص',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Semantic Similarity

Datasets: sts17-arabic and sts17-arabic-final
Evaluated with EmbeddingSimilarityEvaluator

Metric	sts17-arabic	sts17-arabic-final
pearson_cosine	0.8012	0.8012
spearman_cosine	0.803	0.8031

Training Details

Training Dataset

Unnamed Dataset

Size: 685,672 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 5 tokens
mean: 19.49 tokens
max: 83 tokens
min: 4 tokens
mean: 15.81 tokens
max: 70 tokens

Samples:

anchor	positive
`فتاة في قميص أزرق تمشي مع رجل.`	`الفتاة ترتدي قميصاً أزرق`
`ما هو أفضل ماجستير في إدارة الأعمال أو كاليفورنيا؟`	`ما هو أفضل CA أو ماجستير في الإدارة؟`
`الناس يبنيون منزلاً`	`الأفراد يقومون ببناء منزل.`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Evaluation Dataset

Unnamed Dataset

Size: 15,000 evaluation samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 5 tokens
mean: 19.16 tokens
max: 75 tokens
min: 4 tokens
mean: 15.45 tokens
max: 85 tokens

Samples:

anchor	positive
`ثلاثة رجال أعمال يسيرون في شارع مزدحم`	`الناس يتحركون في الشارع`
`أين يمكنني أن أحصل على أفضل نظام رذاذ الحريق في سيدني؟`	`أين يمكنني الحصول على خدمات رشاشات الحريق ذات الجودة العالية في سيدني؟`
`كم تبلغ مساحة نوفا سكوشا؟`	`كم تصل المساحة الجغرافية لولاية نوفا سكوشا؟`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
gradient_accumulation_steps: 4
learning_rate: 2e-05
warmup_ratio: 0.1
fp16: True
dataloader_drop_last: True
load_best_model_at_end: True
optim: adamw_torch_fused
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 4
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 3
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
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: True
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: True
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: True
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}
tp_size: 0
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}
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
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
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	sts17-arabic_spearman_cosine	sts17-arabic-final_spearman_cosine
0.0747	100	5.7187	-	-	-
0.1494	200	1.199	-	-	-
0.2240	300	1.0422	-	-	-
0.2987	400	0.9514	-	-	-
0.3734	500	0.9002	0.0478	0.8091	-
0.4481	600	0.848	-	-	-
0.5228	700	0.8298	-	-	-
0.5975	800	0.7915	-	-	-
0.6721	900	0.7906	-	-	-
0.7468	1000	0.7534	0.0375	0.7950	-
0.8215	1100	0.7384	-	-	-
0.8962	1200	0.7252	-	-	-
0.9709	1300	0.7311	-	-	-
1.0456	1400	0.7006	-	-	-
1.1202	1500	0.6611	0.0334	0.8026	-
1.1949	1600	0.6279	-	-	-
1.2696	1700	0.6072	-	-	-
1.3443	1800	0.596	-	-	-
1.4190	1900	0.5614	-	-	-
1.4937	2000	0.5721	0.0300	0.8041	-
1.5683	2100	0.5681	-	-	-
1.6430	2200	0.5531	-	-	-
1.7177	2300	0.5564	-	-	-
1.7924	2400	0.564	-	-	-
1.8671	2500	0.5395	0.0288	0.8066	-
1.9417	2600	0.5729	-	-	-
2.0164	2700	0.5436	-	-	-
2.0911	2800	0.5365	-	-	-
2.1658	2900	0.5087	-	-	-
2.2405	3000	0.4991	0.0267	0.8009	-
2.3152	3100	0.4761	-	-	-
2.3898	3200	0.4711	-	-	-
2.4645	3300	0.4795	-	-	-
2.5392	3400	0.4732	-	-	-
2.6139	3500	0.4735	0.0264	0.8029	-
2.6886	3600	0.483	-	-	-
2.7633	3700	0.4755	-	-	-
2.8379	3800	0.4783	-	-	-
2.9126	3900	0.4854	-	-	-
2.9873	4000	0.4884	0.0260	0.8030	-
3.0	4017	-	-	-	0.8031

Framework Versions

Python: 3.12.7
Sentence Transformers: 3.3.1
Transformers: 4.51.3
PyTorch: 2.6.0+cu124
Accelerate: 1.4.0
Datasets: 3.3.2
Tokenizers: 0.21.1

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}
}

	anchor	positive
type	string	string
details	min: 5 tokens mean: 19.49 tokens max: 83 tokens	min: 4 tokens mean: 15.81 tokens max: 70 tokens

AhmedZaky1/DIMI-embedding-v3

SentenceTransformer based on intfloat/multilingual-e5-base

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Semantic Similarity

Training Details

Training Dataset

Unnamed Dataset

Evaluation Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

MultipleNegativesRankingLoss

AhmedZaky1/
DIMI-embedding-v3