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Qwen3-Embedding-0.6B-turkish-triplet-matryoshka

Name: Qwen3-Embedding-0.6B-turkish-triplet-matryoshka
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Qwen3-Embedding-0.6B Türkçe Triplet Matryoshka

This is a sentence-transformers model finetuned from Qwen/Qwen3-Embedding-0.6B on the vodex-turkish-triplets dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

⚠️ Domain-Specific Warning

This model was fine-tuned on Turkish data specifically sourced from the telecommunications domain.
While it performs well on telecom-related tasks such as mobile services, billing, campaigns, and subscription details, it may not generalize well to other domains.
Please assess its performance carefully before applying it outside of telecommunications use cases.

Model Description

Model Type: Sentence Transformer
Base model: Qwen/Qwen3-Embedding-0.6B
Maximum Sequence Length: 32768 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- vodex-turkish-triplets
Language: tr
License: apache-2.0

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': 32768, 'do_lower_case': False}) with Transformer model: Qwen3Model 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, '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': True, '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("seroe/Qwen3-Embedding-0.6B-turkish-triplet-matryoshka")
# Run inference
sentences = [
    "Vodafone Net'in internet hız garantisi var mı?",
    'Vodafone Net, internet hızını garanti etmemekte, bu hız abonenin hattının uygunluğuna ve santrale olan mesafeye bağlı olarak değişiklik göstermektedir.',
    'Vodafone Net, tüm abonelerine en az 100 Mbps hız garantisi vermektedir.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

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

Evaluation

Metrics

Triplet

Datasets: tr-triplet-dev-1024d and all-nli-test-1024d
Evaluated with TripletEvaluator with these parameters:
json
1{ 2 "truncate_dim": 1024 3}

Metric	tr-triplet-dev-1024d	all-nli-test-1024d
cosine_accuracy	0.9673	0.9764

Triplet

Datasets: tr-triplet-dev-768d and all-nli-test-768d
Evaluated with TripletEvaluator with these parameters:
json
1{ 2 "truncate_dim": 768 3}

Metric	tr-triplet-dev-768d	all-nli-test-768d
cosine_accuracy	0.969	0.9759

Triplet

Datasets: tr-triplet-dev-512d and all-nli-test-512d
Evaluated with TripletEvaluator with these parameters:
json
1{ 2 "truncate_dim": 512 3}

Metric	tr-triplet-dev-512d	all-nli-test-512d
cosine_accuracy	0.9718	0.9749

Triplet

Datasets: tr-triplet-dev-256d and all-nli-test-256d
Evaluated with TripletEvaluator with these parameters:
json
1{ 2 "truncate_dim": 256 3}

Metric	tr-triplet-dev-256d	all-nli-test-256d
cosine_accuracy	0.9688	0.9756

Triplet

Dataset: tr-triplet-dev-1024d
Evaluated with TripletEvaluator with these parameters:
json
1{ 2 "truncate_dim": 1024 3}

Metric	Value
cosine_accuracy	0.9777

Triplet

Dataset: tr-triplet-dev-768d
Evaluated with TripletEvaluator with these parameters:
json
1{ 2 "truncate_dim": 768 3}

Metric	Value
cosine_accuracy	0.9777

Triplet

Dataset: tr-triplet-dev-512d
Evaluated with TripletEvaluator with these parameters:
json
1{ 2 "truncate_dim": 512 3}

Metric	Value
cosine_accuracy	0.9782

Triplet

Dataset: tr-triplet-dev-256d
Evaluated with TripletEvaluator with these parameters:
json
1{ 2 "truncate_dim": 256 3}

Metric	Value
cosine_accuracy	0.9772

Training Details

Training Dataset

vodex-turkish-triplets

Dataset: vodex-turkish-triplets at 0c9fab0
Size: 70,941 training samples
Columns: query, positive, and negative
Approximate statistics based on the first 1000 samples:
query positive negative
type string string string
details
min: 5 tokens
mean: 17.91 tokens
max: 75 tokens
min: 14 tokens
mean: 38.38 tokens
max: 102 tokens
min: 12 tokens
mean: 29.89 tokens
max: 59 tokens

Samples:

query	positive	negative
`Kampanya tarihleri ve katılım şartları`	`Kampanya, 11 Ekim 2018'de başlayıp 29 Ekim 2018'de sona erecek. Katılımcıların belirli bilgileri doldurması ve Vodafone Müzik pass veya Video pass sahibi olmaları gerekiyor.`	`Kampanya, sadece İstanbul'daki kullanıcılar için geçerli olup, diğer şehirlerden katılım mümkün değildir.`
`Taahhüt süresi dolmadan başka bir kampanyaya geçiş yapılırsa ne olur?`	`Eğer abone taahhüt süresi dolmadan başka bir kampanyaya geçerse, bu durumda önceki kampanya süresince sağlanan indirimler ve diğer faydalar, iptal tarihinden sonraki fatura ile tahsil edilecektir.`	`Aboneler, taahhüt süresi dolmadan başka bir kampanyaya geçtiklerinde, yeni kampanyadan faydalanmak için ek bir ücret ödemek zorundadırlar.`
`FreeZone üyeliğimi nasıl sorgulayabilirim?`	`Üyeliğinizi sorgulamak için FREEZONESORGU yazarak 1525'e SMS gönderebilirsiniz.`	`Üyeliğinizi sorgulamak için Vodafone mağazasına gitmeniz gerekmektedir.`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "CachedMultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 1024, 5 768, 6 512, 7 256 8 ], 9 "matryoshka_weights": [ 10 1, 11 1, 12 1, 13 1 14 ], 15 "n_dims_per_step": -1 16}

Evaluation Dataset

vodex-turkish-triplets

Dataset: vodex-turkish-triplets at 0c9fab0
Size: 3,941 evaluation samples
Columns: query, positive, and negative
Approximate statistics based on the first 1000 samples:
query positive negative
type string string string
details
min: 6 tokens
mean: 17.68 tokens
max: 55 tokens
min: 14 tokens
mean: 38.84 tokens
max: 91 tokens
min: 13 tokens
mean: 30.09 tokens
max: 53 tokens

Samples:

query	positive	negative
`Vodafone Net'e geçiş yaparken bağlantı ücreti var mı?`	`Vodafone Net'e geçişte 264 TL bağlantı ücreti bulunmaktadır ve bu ücret 24 ay boyunca aylık 11 TL olarak faturalandırılmaktadır.`	`Vodafone Net'e geçişte bağlantı ücreti yoktur ve tüm işlemler ücretsizdir.`
`Bağımsız akıllı cihaz kampanyalarının detayları nelerdir?`	`Kampanyalar, farklı cihaz modelleri için aylık ödeme planları sunmaktadır.`	`Vodafone'un kampanyaları, sadece internet paketleri ile ilgilidir.`
`Fibermax hizmeti iptal edilirse ne gibi sonuçlar doğar?`	`İptal işlemi taahhüt süresi bitmeden yapılırsa, indirimler ve ücretsiz hizmet bedelleri ödenmelidir.`	`Fibermax hizmeti iptal edildiğinde, kullanıcıdan hiçbir ücret talep edilmez.`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "CachedMultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 1024, 5 768, 6 512, 7 256 8 ], 9 "matryoshka_weights": [ 10 1, 11 1, 12 1, 13 1 14 ], 15 "n_dims_per_step": -1 16}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 2048
per_device_eval_batch_size: 256
weight_decay: 0.01
num_train_epochs: 2
lr_scheduler_type: cosine
warmup_ratio: 0.05
save_only_model: True
bf16: True
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: 2048
per_device_eval_batch_size: 256
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: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 2
max_steps: -1
lr_scheduler_type: cosine
lr_scheduler_kwargs: {}
warmup_ratio: 0.05
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: True
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: True
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}
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: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	tr-triplet-dev-1024d_cosine_accuracy	tr-triplet-dev-768d_cosine_accuracy	tr-triplet-dev-512d_cosine_accuracy	tr-triplet-dev-256d_cosine_accuracy	all-nli-test-1024d_cosine_accuracy	all-nli-test-768d_cosine_accuracy	all-nli-test-512d_cosine_accuracy	all-nli-test-256d_cosine_accuracy
0.3429	12	10.2876	3.2218	0.9145	0.9211	0.9262	0.9229	-	-	-	-
0.6857	24	6.1342	2.5250	0.9531	0.9561	0.9571	0.9553	-	-	-	-
0.3429	12	4.8969	2.3174	0.9597	0.9632	0.9617	0.9632	-	-	-	-
0.6857	24	4.2031	2.0383	0.9673	0.9690	0.9718	0.9688	-	-	-	-
0.3429	12	3.3893	2.3286	0.9650	0.9655	0.9652	0.9652	-	-	-	-
0.6857	24	3.0878	2.1443	0.9728	0.9739	0.9749	0.9736	-	-	-	-
1.0286	36	3.504	1.8128	0.9708	0.9716	0.9716	0.9723	-	-	-	-
1.3714	48	2.4279	1.8915	0.9779	0.9774	0.9782	0.9777	-	-	-	-
1.7143	60	2.2489	1.8638	0.9777	0.9777	0.9782	0.9772	-	-	-	-
-1	-1	-	-	-	-	-	-	0.9764	0.9759	0.9749	0.9756

Framework Versions

Python: 3.10.12
Sentence Transformers: 4.2.0.dev0
Transformers: 4.52.3
PyTorch: 2.7.0+cu126
Accelerate: 1.7.0
Datasets: 3.6.0
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",
}

MatryoshkaLoss

bibtex

@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}
}

CachedMultipleNegativesRankingLoss

bibtex

@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

	query	positive	negative
type	string	string	string
details	min: 5 tokens mean: 17.91 tokens max: 75 tokens	min: 14 tokens mean: 38.38 tokens max: 102 tokens	min: 12 tokens mean: 29.89 tokens max: 59 tokens