orhanxakarsu/sentence-distilbert-turkish

distilbert-base-turkish-case trained on AllNLI Turkish translate triplets

This is a sentence-transformers model finetuned from dbmdz/distilbert-base-turkish-cased. 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: dbmdz/distilbert-base-turkish-cased
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• 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': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel 
  (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})
)

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("orhanxakarsu/sentence-distilbert-turkish")
# Run inference
sentences = [
    "İki kadın, Çin'deki bir markette bir ürüne bakıyor.",
    'Alışveriş yapan iki kadın',
    'Kadınlar bir spor salonunda çalışıyorlar.',
]
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

Triplet

• Dataset: all-nli-turkish-dev
• Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9802

Training Details

Training Dataset

Unnamed Dataset

• Size: 814,596 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 3 tokens mean: 18.16 tokens max: 91 tokens	min: 4 tokens mean: 10.54 tokens max: 136 tokens	min: 4 tokens mean: 10.73 tokens max: 29 tokens

• Samples:

  anchor	positive	negative
  Beyaz gömlekli ve güneş gözlüklü bir kadın, kucağında bir bebekle dışarıda bir sandalyede oturuyor.	Bebek yerden yukarıda oturuyor	Adam bir top atıyor
  Mavi yakalı gömlek giyen ve kazaklı bir adam ve beyaz gömlek giyen hasır şapka takan bir kadın.	Yan yana bir erkek ve bir kadın var.	Evli bir çift akşam yemeği yiyor.
  Adam içeride.	Siyah fötr şapkalı bir adam bir arenada boğaya biniyor.	Yeşil üniforma giyen beş subayla birlikte taş bir binanın önünde cep telefonuyla konuşan bir papaz; ikisi ayakta, diğerleri oturuyor.

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 8,229 evaluation samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 4 tokens mean: 17.91 tokens max: 80 tokens	min: 4 tokens mean: 10.62 tokens max: 35 tokens	min: 4 tokens mean: 11.01 tokens max: 33 tokens

• Samples:

  anchor	positive	negative
  Patlamanın büyüklüğünün güçlü bir örneği, Haragosha Tapınağı'nda bulunur, burada tapınağın kemerinin üst crosebar'ını görebilirsiniz, geri kalanı sertleşmiş lav tarafından batırılmıştır.	Patlamanın büyüklüğünün sonucu Haragosha Tapınağı'nda görülüyor.	Haragosha Tapınağı bu güne kadar tamamen sağlamdır.
  Arkeolojik kazı yapan iki kişi.	Kazı yapan insanlar var.	Kimse kazmıyor.
  İşçiler, Martins'in ünlü Louisiana sosis satıcısı çadırının önünde sıraya giren müşterilere hizmet veriyor	Müşteriler bir satıcı çadırının önünde sıraya giriyor.	Pamuk şeker yiyen bir grup insan var.

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• learning_rate: 2e-05
• num_train_epochs: 10
• warmup_ratio: 0.1
• fp16: 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: 64
• per_device_eval_batch_size: 64
• 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: 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: 10
• 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: 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: False
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• 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
• dispatch_batches: None
• split_batches: 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
• eval_use_gather_object: False
• prompts: None
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss	all-nli-turkish-dev_cosine_accuracy
0	0	-	-	0.5808
0.0786	1000	3.5327	1.9481	0.7607
0.1571	2000	1.5833	1.2787	0.8260
0.2357	3000	1.2338	1.0960	0.8533
0.3142	4000	1.1031	0.9897	0.8695
0.3928	5000	0.998	0.9077	0.8793
0.4714	6000	0.9412	0.8434	0.8914
0.5499	7000	0.8703	0.7904	0.8982
0.6285	8000	0.8094	0.7311	0.9068
0.7070	9000	0.7653	0.6894	0.9086
0.7856	10000	0.7248	0.6509	0.9162
0.8642	11000	0.673	0.6145	0.9205
0.9427	12000	0.6514	0.5762	0.9273
1.0213	13000	0.6259	0.5463	0.9334
1.0999	14000	0.5874	0.5276	0.9332
1.1784	15000	0.5518	0.5053	0.9366
1.2570	16000	0.5277	0.4783	0.9391
1.3355	17000	0.5075	0.4571	0.9419
1.4141	18000	0.4906	0.4379	0.9454
1.4927	19000	0.475	0.4234	0.9465
1.5712	20000	0.447	0.4046	0.9499
1.6498	21000	0.4307	0.3908	0.9508
1.7283	22000	0.4126	0.3773	0.9548
1.8069	23000	0.3985	0.3654	0.9564
1.8855	24000	0.3748	0.3582	0.9560
1.9640	25000	0.3675	0.3449	0.9581
2.0426	26000	0.3545	0.3390	0.9586
2.1211	27000	0.3456	0.3335	0.9595
2.1997	28000	0.3295	0.3255	0.9626
2.2783	29000	0.3198	0.3146	0.9624
2.3568	30000	0.3107	0.3101	0.9642
2.4354	31000	0.3139	0.3014	0.9665
2.5139	32000	0.2982	0.3005	0.9659
2.5925	33000	0.2903	0.2891	0.9663
2.6711	34000	0.2778	0.2859	0.9662
2.7496	35000	0.2731	0.2812	0.9667
2.8282	36000	0.2613	0.2757	0.9677
2.9067	37000	0.2566	0.2680	0.9689
2.9853	38000	0.2488	0.2674	0.9699
3.0639	39000	0.2434	0.2594	0.9694
3.1424	40000	0.2375	0.2574	0.9705
3.2210	41000	0.2295	0.2553	0.9706
3.2996	42000	0.223	0.2501	0.9703
3.3781	43000	0.2209	0.2455	0.9719
3.4567	44000	0.2211	0.2409	0.9711
3.5352	45000	0.2097	0.2396	0.9728
3.6138	46000	0.2068	0.2345	0.9734
3.6924	47000	0.1994	0.2298	0.9731
3.7709	48000	0.1986	0.2299	0.9730
3.8495	49000	0.1878	0.2271	0.9728
3.9280	50000	0.1872	0.2244	0.9739
4.0066	51000	0.1821	0.2249	0.9734
4.0852	52000	0.1823	0.2188	0.9739
4.1637	53000	0.1736	0.2176	0.9748
4.2423	54000	0.1691	0.2152	0.9745
4.3208	55000	0.1665	0.2148	0.9753
4.3994	56000	0.1663	0.2133	0.9748
4.4780	57000	0.1666	0.2123	0.9755
4.5565	58000	0.1589	0.2082	0.9758
4.6351	59000	0.155	0.2053	0.9762
4.7136	60000	0.155	0.2037	0.9762
4.7922	61000	0.1536	0.2031	0.9764
4.8708	62000	0.1443	0.2020	0.9759
4.9493	63000	0.146	0.1999	0.9752
5.0279	64000	0.1417	0.1969	0.9764
5.1064	65000	0.1407	0.1966	0.9761
5.1850	66000	0.1342	0.1981	0.9757
5.2636	67000	0.1342	0.1933	0.9768
5.3421	68000	0.1312	0.1944	0.9758
5.4207	69000	0.1329	0.1932	0.9772
5.4993	70000	0.1304	0.1908	0.9768
5.5778	71000	0.1247	0.1880	0.9772
5.6564	72000	0.1221	0.1861	0.9779
5.7349	73000	0.1225	0.1831	0.9784
5.8135	74000	0.1205	0.1854	0.9790
5.8921	75000	0.1152	0.1815	0.9789
5.9706	76000	0.1161	0.1827	0.9782
6.0492	77000	0.1151	0.1819	0.9781
6.1277	78000	0.113	0.1818	0.9780
6.2063	79000	0.1102	0.1823	0.9784
6.2849	80000	0.1067	0.1798	0.9780
6.3634	81000	0.1067	0.1782	0.9790
6.4420	82000	0.1116	0.1779	0.9782
6.5205	83000	0.107	0.1752	0.9782
6.5991	84000	0.1039	0.1739	0.9792
6.6777	85000	0.1013	0.1728	0.9789
6.7562	86000	0.1029	0.1713	0.9786
6.8348	87000	0.0972	0.1721	0.9791
6.9133	88000	0.0991	0.1703	0.9790
6.9919	89000	0.0955	0.1708	0.9791
7.0705	90000	0.097	0.1715	0.9786
7.1490	91000	0.0941	0.1716	0.9793
7.2276	92000	0.0922	0.1712	0.9795
7.3062	93000	0.0921	0.1706	0.9789
7.3847	94000	0.091	0.1691	0.9793
7.4633	95000	0.0942	0.1689	0.9787
7.5418	96000	0.0905	0.1678	0.9790
7.6204	97000	0.0871	0.1664	0.9792
7.6990	98000	0.0859	0.1666	0.9793
7.7775	99000	0.0876	0.1656	0.9785
7.8561	100000	0.084	0.1643	0.9795
7.9346	101000	0.0853	0.1654	0.9795
8.0132	102000	0.083	0.1640	0.9789
8.0918	103000	0.0849	0.1637	0.9795
8.1703	104000	0.0816	0.1626	0.9797
8.2489	105000	0.0803	0.1627	0.9796
8.3274	106000	0.0802	0.1623	0.9796
8.4060	107000	0.0808	0.1622	0.9798
8.4846	108000	0.0836	0.1632	0.9792
8.5631	109000	0.0791	0.1612	0.9796
8.6417	110000	0.0761	0.1609	0.9798
8.7202	111000	0.0782	0.1604	0.9797
8.7988	112000	0.0784	0.1604	0.9803
8.8774	113000	0.0737	0.1600	0.9804
8.9559	114000	0.0762	0.1602	0.9799
9.0345	115000	0.0764	0.1597	0.9802
9.1130	116000	0.0761	0.1600	0.9799
9.1916	117000	0.0729	0.1592	0.9797
9.2702	118000	0.0728	0.1595	0.9803
9.3487	119000	0.0722	0.1590	0.9798
9.4273	120000	0.0745	0.1591	0.9797
9.5059	121000	0.0741	0.1591	0.9798
9.5844	122000	0.0715	0.1587	0.9797
9.6630	123000	0.0719	0.1581	0.9799
9.7415	124000	0.0716	0.1578	0.9799
9.8201	125000	0.0714	0.1582	0.9801
9.8987	126000	0.0712	0.1579	0.9803
9.9772	127000	0.0707	0.1581	0.9802

Framework Versions

• Python: 3.12.4
• Sentence Transformers: 3.3.1
• Transformers: 4.44.2
• PyTorch: 2.4.1+cu124
• Accelerate: 0.33.0
• Datasets: 3.1.0
• 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",
}

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}
}