metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:498670
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
base_model: Alibaba-NLP/gte-multilingual-base
widget:
  - source_sentence: كم يبغ عدد السكان في المملكة المتحدة؟
    sentences:
      - هناك العديد من الناس الحاضرين.
      - كم عدد سكان أوكرانيا؟
      - لماذا باراك أوباما غير مؤهل للترشح في انتخابات الرئاسة لعام 2016؟
  - source_sentence: ماذا يجب أن أعرف عن ممارسة الأعمال التجارية في بلدك كرائد أعمال؟
    sentences:
      - >-
        إذا كان بإمكانك العيش في أي مكان في العالم لمدة عام، أين سيكون ذلك
        ولماذا؟
      - ماذا يجب أن أعطي صديقي في عيد الميلاد؟
      - ماذا يجب أن أعرف عن ممارسة الأعمال التجارية في بلدك؟
  - source_sentence: الرجل يرسم
    sentences:
      - رجل يستخدم الطلاء الرذاذ لرسم صورة على الحائط.
      - العرض مقرّر غداً.
      - مساء من الترفيه تحت النجوم هو أساسا جنوب كاليفورنيا.
  - source_sentence: لماذا لا يزال دونالد ترامب "يتجنب" قضية إقرار ضريبة الدخل؟
    sentences:
      - الحديقة لديها بوابة
      - >-
        لماذا لا يبدأ ترامب في قول "الحقيقة" عن طريق الإفصاح عن إقراراته
        الضريبية؟
      - كيف يمكنني التحقق من حسابي على إنستغرام مع علامة زرقاء؟
  - source_sentence: لا أعتقد ذلك
    sentences:
      - رجل واحد في قميص برتقالي يرتدي خوذة بيضاء يركب دراجة.
      - هناك أشخاص يأكلون في مطعم.
      - أخشى لا يا سيدي
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
model-index:
  - name: SentenceTransformer based on Alibaba-NLP/gte-multilingual-base
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: arabic sts17
          type: arabic-sts17
        metrics:
          - type: pearson_cosine
            value: 0.8112776989727821
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8156442694344616
            name: Spearman Cosine

SentenceTransformer based on Alibaba-NLP/gte-multilingual-base

This is a sentence-transformers model finetuned from Alibaba-NLP/gte-multilingual-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: Alibaba-NLP/gte-multilingual-base
Maximum Sequence Length: 8192 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': 8192, 'do_lower_case': False}) with Transformer model: NewModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'لا أعتقد ذلك',
    'أخشى لا يا سيدي',
    'رجل واحد في قميص برتقالي يرتدي خوذة بيضاء يركب دراجة.',
]
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

Dataset: arabic-sts17
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8113
spearman_cosine	0.8156

Training Details

Training Dataset

Unnamed Dataset

Size: 498,670 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: 19.59 tokens
max: 82 tokens

min: 4 tokens
mean: 13.98 tokens
max: 69 tokens

	sentence_0	sentence_1
type	string	string
details	min: 4 tokens mean: 19.59 tokens max: 82 tokens	min: 4 tokens mean: 13.98 tokens max: 69 tokens

Samples:

sentence_0	sentence_1
`ولد صغير يرتدي ملابس زرقاء يرتدي حذاء`	`الصبي الصغير يرتدي ملابسه`
`كيف يتم بناء كاميرات المراقبة؟`	`ما هي كاميرا المراقبة؟`
`لماذا الطاقة الإجمالية للكون صفر؟`	`إذا كان إجمالي الطاقة في الكون صفر، فهل يعني ذلك أن هناك طريقة لـ "صنع" المادة/الطاقة من خلال صنع نوع من النظير؟`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        768,
        384,
        128
    ],
    "matryoshka_weights": [
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 24
per_device_eval_batch_size: 24
fp16: True
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 24
per_device_eval_batch_size: 24
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: 3
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: 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: 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}
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
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: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss	arabic-sts17_spearman_cosine
0.0481	500	1.6592	-
0.0963	1000	1.177	-
0.1444	1500	1.0053	-
0.1925	2000	0.9125	0.8135
0.2406	2500	0.8212	-
0.2888	3000	0.8204	-
0.3369	3500	0.7696	-
0.3850	4000	0.7501	0.8089
0.4332	4500	0.7118	-
0.4813	5000	0.7073	-
0.5294	5500	0.6772	-
0.5775	6000	0.6637	0.8085
0.6257	6500	0.6507	-
0.6738	7000	0.605	-
0.7219	7500	0.6076	-
0.7700	8000	0.6076	0.8060
0.8182	8500	0.5594	-
0.8663	9000	0.5928	-
0.9144	9500	0.5587	-
0.9626	10000	0.5736	0.8099
1.0	10389	-	0.8122
1.0107	10500	0.555	-
1.0588	11000	0.5233	-
1.1069	11500	0.5216	-
1.1551	12000	0.5176	0.8015
1.2032	12500	0.4865	-
1.2513	13000	0.4907	-
1.2995	13500	0.5079	-
1.3476	14000	0.4991	0.8027
1.3957	14500	0.4834	-
1.4438	15000	0.4626	-
1.4920	15500	0.4442	-
1.5401	16000	0.4768	0.8079
1.5882	16500	0.4459	-
1.6363	17000	0.4409	-
1.6845	17500	0.4434	-
1.7326	18000	0.4264	0.8041
1.7807	18500	0.4341	-
1.8289	19000	0.4143	-
1.8770	19500	0.4304	-
1.9251	20000	0.4314	0.8133
1.9732	20500	0.448	-
2.0	20778	-	0.8116
2.0214	21000	0.3985	-
2.0695	21500	0.3854	-
2.1176	22000	0.3875	0.8095
2.1658	22500	0.4139	-
2.2139	23000	0.3956	-
2.2620	23500	0.3856	-
2.3101	24000	0.3816	0.8110
2.3583	24500	0.3732	-
2.4064	25000	0.3662	-
2.4545	25500	0.3773	-
2.5026	26000	0.3703	0.8058
2.5508	26500	0.3666	-
2.5989	27000	0.369	-
2.6470	27500	0.3612	-
2.6952	28000	0.3444	0.8135
2.7433	28500	0.3667	-
2.7914	29000	0.3707	-
2.8395	29500	0.3698	-
2.8877	30000	0.3658	0.8156

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

@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}
}