metadata
language:
- ar
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:557850
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: ذكر متوازن بعناية يقف على قدم واحدة بالقرب من منطقة شاطئ المحيط النظيفة
sentences:
- رجل يقدم عرضاً
- هناك رجل بالخارج قرب الشاطئ
- رجل يجلس على أريكه
- source_sentence: رجل يقفز إلى سريره القذر
sentences:
- السرير قذر.
- رجل يضحك أثناء غسيل الملابس
- الرجل على القمر
- source_sentence: الفتيات بالخارج
sentences:
- امرأة تلف الخيط إلى كرات بجانب كومة من الكرات
- فتيان يركبان في جولة متعة
- >-
ثلاث فتيات يقفون سوية في غرفة واحدة تستمع وواحدة تكتب على الحائط
والثالثة تتحدث إليهن
- source_sentence: الرجل يرتدي قميصاً أزرق.
sentences:
- >-
رجل يرتدي قميصاً أزرق يميل إلى الجدار بجانب الطريق مع شاحنة زرقاء وسيارة
حمراء مع الماء في الخلفية.
- كتاب القصص مفتوح
- رجل يرتدي قميص أسود يعزف على الجيتار.
- source_sentence: يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة شابة.
sentences:
- ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه
- رجل يستلقي على وجهه على مقعد في الحديقة.
- الشاب نائم بينما الأم تقود ابنتها إلى الحديقة
pipeline_tag: sentence-similarity
model-index:
- name: >-
SentenceTransformer based on
sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 256
type: sts-test-256
metrics:
- type: pearson_cosine
value: 0.8264447022356382
name: Pearson Cosine
- type: spearman_cosine
value: 0.8386403752382455
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8219134931449013
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.825509659109493
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8223094468630248
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8260503151751462
name: Spearman Euclidean
- type: pearson_dot
value: 0.6375226884845725
name: Pearson Dot
- type: spearman_dot
value: 0.6287228614640888
name: Spearman Dot
- type: pearson_max
value: 0.8264447022356382
name: Pearson Max
- type: spearman_max
value: 0.8386403752382455
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 128
type: sts-test-128
metrics:
- type: pearson_cosine
value: 0.8209661910768973
name: Pearson Cosine
- type: spearman_cosine
value: 0.8347149482673766
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8082811559854036
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8148314269262763
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8093138512113149
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8156468458613929
name: Spearman Euclidean
- type: pearson_dot
value: 0.5795109620454884
name: Pearson Dot
- type: spearman_dot
value: 0.5760223026552876
name: Spearman Dot
- type: pearson_max
value: 0.8209661910768973
name: Pearson Max
- type: spearman_max
value: 0.8347149482673766
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 64
type: sts-test-64
metrics:
- type: pearson_cosine
value: 0.808708530451336
name: Pearson Cosine
- type: spearman_cosine
value: 0.8217532539767914
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7876121380998453
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7969092304137347
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7902997966909958
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7987635968785215
name: Spearman Euclidean
- type: pearson_dot
value: 0.495047136234386
name: Pearson Dot
- type: spearman_dot
value: 0.49287000679901516
name: Spearman Dot
- type: pearson_max
value: 0.808708530451336
name: Pearson Max
- type: spearman_max
value: 0.8217532539767914
name: Spearman Max
SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 on the Omartificial-Intelligence-Space/arabic-n_li-triplet dataset. It maps sentences & paragraphs to a 384-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: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
- Maximum Sequence Length: 128 tokens
- Output Dimensionality: 384 tokens
- Similarity Function: Cosine Similarity
- Training Dataset:
- Omartificial-Intelligence-Space/arabic-n_li-triplet
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': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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})
)
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("Omartificial-Intelligence-Space/MiniLM-L12-v2-all-nli-triplet")
# Run inference
sentences = [
'يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة شابة.',
'ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه',
'الشاب نائم بينما الأم تقود ابنتها إلى الحديقة',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Semantic Similarity
- Dataset:
sts-test-256
- Evaluated with
EmbeddingSimilarityEvaluator
Metric | Value |
---|---|
pearson_cosine | 0.8264 |
spearman_cosine | 0.8386 |
pearson_manhattan | 0.8219 |
spearman_manhattan | 0.8255 |
pearson_euclidean | 0.8223 |
spearman_euclidean | 0.8261 |
pearson_dot | 0.6375 |
spearman_dot | 0.6287 |
pearson_max | 0.8264 |
spearman_max | 0.8386 |
Semantic Similarity
- Dataset:
sts-test-128
- Evaluated with
EmbeddingSimilarityEvaluator
Metric | Value |
---|---|
pearson_cosine | 0.821 |
spearman_cosine | 0.8347 |
pearson_manhattan | 0.8083 |
spearman_manhattan | 0.8148 |
pearson_euclidean | 0.8093 |
spearman_euclidean | 0.8156 |
pearson_dot | 0.5795 |
spearman_dot | 0.576 |
pearson_max | 0.821 |
spearman_max | 0.8347 |
Semantic Similarity
- Dataset:
sts-test-64
- Evaluated with
EmbeddingSimilarityEvaluator
Metric | Value |
---|---|
pearson_cosine | 0.8087 |
spearman_cosine | 0.8218 |
pearson_manhattan | 0.7876 |
spearman_manhattan | 0.7969 |
pearson_euclidean | 0.7903 |
spearman_euclidean | 0.7988 |
pearson_dot | 0.495 |
spearman_dot | 0.4929 |
pearson_max | 0.8087 |
spearman_max | 0.8218 |
Training Details
Training Dataset
Omartificial-Intelligence-Space/arabic-n_li-triplet
- Dataset: Omartificial-Intelligence-Space/arabic-n_li-triplet
- Size: 557,850 training samples
- Columns:
anchor
,positive
, andnegative
- Approximate statistics based on the first 1000 samples:
anchor positive negative type string string string details - min: 5 tokens
- mean: 10.33 tokens
- max: 52 tokens
- min: 5 tokens
- mean: 13.21 tokens
- max: 49 tokens
- min: 5 tokens
- mean: 15.32 tokens
- max: 53 tokens
- Samples:
anchor positive negative شخص على حصان يقفز فوق طائرة معطلة
شخص في الهواء الطلق، على حصان.
شخص في مطعم، يطلب عجة.
أطفال يبتسمون و يلوحون للكاميرا
هناك أطفال حاضرون
الاطفال يتجهمون
صبي يقفز على لوح التزلج في منتصف الجسر الأحمر.
الفتى يقوم بخدعة التزلج
الصبي يتزلج على الرصيف
- Loss:
MatryoshkaLoss
with these parameters:{ "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1 ], "n_dims_per_step": -1 }
Evaluation Dataset
Omartificial-Intelligence-Space/arabic-n_li-triplet
- Dataset: Omartificial-Intelligence-Space/arabic-n_li-triplet
- Size: 6,584 evaluation samples
- Columns:
anchor
,positive
, andnegative
- Approximate statistics based on the first 1000 samples:
anchor positive negative type string string string details - min: 5 tokens
- mean: 21.86 tokens
- max: 105 tokens
- min: 4 tokens
- mean: 10.22 tokens
- max: 49 tokens
- min: 4 tokens
- mean: 11.2 tokens
- max: 33 tokens
- Samples:
anchor positive negative امرأتان يتعانقان بينما يحملان حزمة
إمرأتان يحملان حزمة
الرجال يتشاجرون خارج مطعم
طفلين صغيرين يرتديان قميصاً أزرق، أحدهما يرتدي الرقم 9 والآخر يرتدي الرقم 2 يقفان على خطوات خشبية في الحمام ويغسلان أيديهما في المغسلة.
طفلين يرتديان قميصاً مرقماً يغسلون أيديهم
طفلين يرتديان سترة يذهبان إلى المدرسة
رجل يبيع الدونات لعميل خلال معرض عالمي أقيم في مدينة أنجليس
رجل يبيع الدونات لعميل
امرأة تشرب قهوتها في مقهى صغير
- Loss:
MatryoshkaLoss
with these parameters:{ "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1 ], "n_dims_per_step": -1 }
Training Hyperparameters
Non-Default Hyperparameters
per_device_train_batch_size
: 64per_device_eval_batch_size
: 64num_train_epochs
: 1warmup_ratio
: 0.1fp16
: Truebatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseprediction_loss_only
: Trueper_device_train_batch_size
: 64per_device_eval_batch_size
: 64per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 1max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Truefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-64_spearman_cosine |
---|---|---|---|---|---|
0.0229 | 200 | 6.2204 | - | - | - |
0.0459 | 400 | 4.9559 | - | - | - |
0.0688 | 600 | 4.7835 | - | - | - |
0.0918 | 800 | 4.2725 | - | - | - |
0.1147 | 1000 | 4.291 | - | - | - |
0.1377 | 1200 | 4.0704 | - | - | - |
0.1606 | 1400 | 3.7962 | - | - | - |
0.1835 | 1600 | 3.7447 | - | - | - |
0.2065 | 1800 | 3.569 | - | - | - |
0.2294 | 2000 | 3.5373 | - | - | - |
0.2524 | 2200 | 3.608 | - | - | - |
0.2753 | 2400 | 3.5609 | - | - | - |
0.2983 | 2600 | 3.5231 | - | - | - |
0.3212 | 2800 | 3.3312 | - | - | - |
0.3442 | 3000 | 3.4803 | - | - | - |
0.3671 | 3200 | 3.3552 | - | - | - |
0.3900 | 3400 | 3.3024 | - | - | - |
0.4130 | 3600 | 3.2559 | - | - | - |
0.4359 | 3800 | 3.1882 | - | - | - |
0.4589 | 4000 | 3.227 | - | - | - |
0.4818 | 4200 | 3.0889 | - | - | - |
0.5048 | 4400 | 3.0861 | - | - | - |
0.5277 | 4600 | 3.0178 | - | - | - |
0.5506 | 4800 | 3.231 | - | - | - |
0.5736 | 5000 | 3.1593 | - | - | - |
0.5965 | 5200 | 3.1101 | - | - | - |
0.6195 | 5400 | 3.1307 | - | - | - |
0.6424 | 5600 | 3.1265 | - | - | - |
0.6654 | 5800 | 3.1116 | - | - | - |
0.6883 | 6000 | 3.1417 | - | - | - |
0.7113 | 6200 | 3.0862 | - | - | - |
0.7342 | 6400 | 2.9652 | - | - | - |
0.7571 | 6600 | 2.8466 | - | - | - |
0.7801 | 6800 | 2.271 | - | - | - |
0.8030 | 7000 | 2.046 | - | - | - |
0.8260 | 7200 | 1.9634 | - | - | - |
0.8489 | 7400 | 1.8875 | - | - | - |
0.8719 | 7600 | 1.7655 | - | - | - |
0.8948 | 7800 | 1.6874 | - | - | - |
0.9177 | 8000 | 1.7315 | - | - | - |
0.9407 | 8200 | 1.6674 | - | - | - |
0.9636 | 8400 | 1.6574 | - | - | - |
0.9866 | 8600 | 1.6142 | - | - | - |
1.0 | 8717 | - | 0.8347 | 0.8386 | 0.8218 |
Framework Versions
- Python: 3.9.18
- Sentence Transformers: 3.0.1
- Transformers: 4.40.0
- PyTorch: 2.2.2+cu121
- Accelerate: 0.26.1
- Datasets: 2.19.0
- Tokenizers: 0.19.1
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",
}
MatryoshkaLoss
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
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}
}