mertcobanov/mpnet-base-all-nli-triplet-turkish-v4-dgx

SentenceTransformer based on microsoft/mpnet-base

This is a sentence-transformers model finetuned from microsoft/mpnet-base on the all-nli-triplets-turkish dataset. 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: microsoft/mpnet-base
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • all-nli-triplets-turkish
• Languages: en, tr

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: MPNetModel 
  (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("mertcobanov/mpnet-base-all-nli-triplet-turkish-v4-dgx")
# Run inference
sentences = [
    'Böyle şeyler görmek ve eğer yapabileceğiniz en küçük bir şey varsa, bu yardımcı olur.',
    'Böyle bir şeyi gözlemlemek ve yapıp yapamayacağınızı bilmek için.',
    'Böyle bir şeyi görmek kötü, eğer yapabiliyorsanız buna hiç katkıda bulunmayın.',
]
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

• Datasets: all-nli-dev-turkish and all-nli-test-turkish
• Evaluated with TripletEvaluator

Metric	all-nli-dev-turkish	all-nli-test-turkish
cosine_accuracy	0.7764	0.7741

Training Details

Training Dataset

all-nli-triplets-turkish

• Dataset: all-nli-triplets-turkish at 13554fd
• Size: 120,781 training samples
• Columns: anchor_translated, positive_translated, and negative_translated
• Approximate statistics based on the first 1000 samples:

  	anchor_translated	positive_translated	negative_translated
  type	string	string	string
  details	min: 3 tokens mean: 11.77 tokens max: 40 tokens	min: 3 tokens mean: 11.1 tokens max: 46 tokens	min: 2 tokens mean: 12.41 tokens max: 44 tokens

• Samples:

  anchor_translated	positive_translated	negative_translated
  Bir kişi, bir atın üzerinde, bozulmuş bir uçağın üzerinden atlıyor.	Bir kişi dışarıda, bir atın üzerinde.	Bir kişi bir lokantada omlet siparişi veriyor.
  Bir Küçük Lig takımı, bir oyuncunun bir üsse kayarak girmeye çalıştığı sırada onu yakalamaya çalışıyor.	Bir takım bir koşucuyu dışarı atmaya çalışıyor.	Bir takım Satürn'de beyzbol oynuyor.
  Kadın beyaz giyiyor.	Beyaz bir ceket giymiş bir kadın bir tekerlekli sandalyeyi itiyor.	Siyah giyinmiş bir adam, siyah giyinmiş bir kadını kucaklıyor.

• Loss: MultipleNegativesRankingLoss with these parameters:

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

Evaluation Dataset

all-nli-triplets-turkish

• Dataset: all-nli-triplets-turkish at 13554fd
• Size: 6,584 evaluation samples
• Columns: anchor_translated, positive_translated, and negative_translated
• Approximate statistics based on the first 1000 samples:

  	anchor_translated	positive_translated	negative_translated
  type	string	string	string
  details	min: 2 tokens mean: 22.3 tokens max: 135 tokens	min: 1 tokens mean: 10.92 tokens max: 41 tokens	min: 3 tokens mean: 10.81 tokens max: 34 tokens

• Samples:

  anchor_translated	positive_translated	negative_translated
  Ayrıca, bu özel tüketim vergileri, diğer vergiler gibi, hükümetin ödeme zorunluluğunu sağlama yetkisini kullanarak belirlenir.	Hükümetin ödeme zorlaması, özel tüketim vergilerinin nasıl hesaplandığını belirler.	Özel tüketim vergileri genel kuralın bir istisnasıdır ve aslında GSYİH payına dayalı olarak belirlenir.
  Gri bir sweatshirt giymiş bir sanatçı, canlı renklerde bir kasaba tablosu üzerinde çalışıyor.	Bir ressam gri giysiler içinde bir kasabanın resmini yapıyor.	Bir kişi bir beyzbol sopası tutuyor ve gelen bir atış için planda bekliyor.
  İmkansız.	Yapılamaz.	Tamamen mümkün.

• 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
• 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
• 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
• 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	all-nli-dev-turkish_cosine_accuracy	all-nli-test-turkish_cosine_accuracy
0	0	-	-	0.5729	-
0.2119	100	6.6103	4.5154	0.6970	-
0.4237	200	5.1602	3.7328	0.7195	-
0.6356	300	4.4533	3.3389	0.7372	-
0.8475	400	3.4465	3.6044	0.7187	-
1.0572	500	2.6977	3.3043	0.7418	-
1.2691	600	3.8142	3.2066	0.7512	-
1.4809	700	3.4333	3.0716	0.7508	-
1.6928	800	3.1488	2.9590	0.7553	-
1.9047	900	1.8677	3.2416	0.7442	-
2.1144	1000	2.2034	2.9323	0.7634	-
2.3263	1100	2.9834	2.9406	0.7669	-
2.5381	1200	2.6785	2.8607	0.7672	-
2.75	1300	2.5096	2.8939	0.7684	-
2.9619	1400	0.876	3.2539	0.7416	-
3.1716	1500	2.3355	2.7503	0.7758	-
3.3835	1600	2.4666	2.7920	0.7707	-
3.5953	1700	2.2691	2.7860	0.7729	-
3.8072	1800	1.8024	2.9899	0.7571	-
4.0169	1900	0.6443	3.0993	0.7456	-
4.2288	2000	2.3976	2.7792	0.7811	-
4.4407	2100	2.1145	2.7968	0.7728	-
4.6525	2200	1.9788	2.7243	0.7751	-
4.8644	2300	1.1676	2.9885	0.7567	-
5.0742	2400	1.0009	2.7374	0.7767	-
5.2860	2500	2.1276	2.7822	0.7767	-
5.4979	2600	1.8459	2.7822	0.7760	-
5.7097	2700	1.7659	2.7322	0.7766	-
5.9216	2800	0.5916	3.0191	0.7596	-
6.1314	2900	1.3908	2.6973	0.7772	-
6.3432	3000	1.9257	2.7585	0.7763	-
6.5551	3100	1.6558	2.7350	0.7760	-
6.7669	3200	1.5368	2.7903	0.7722	-
6.9788	3300	0.1968	3.0849	0.7479	-
7.1886	3400	1.8044	2.6626	0.7825	-
7.4004	3500	1.7048	2.7380	0.7790	-
7.6123	3600	1.5666	2.7250	0.7796	-
7.8242	3700	1.0954	2.9620	0.7629	-
8.0339	3800	0.487	2.8900	0.7641	-
8.2458	3900	1.8398	2.7186	0.7796	-
8.4576	4000	1.5659	2.7259	0.7778	-
8.6695	4100	1.4825	2.7007	0.7760	-
8.8814	4200	0.7019	2.9050	0.7675	-
9.0911	4300	0.9278	2.7606	0.7731	-
9.3030	4400	1.766	2.6978	0.7787	-
9.5148	4500	1.4699	2.7114	0.7801	-
9.7267	4600	1.4647	2.7096	0.7799	-
9.9386	4700	0.3321	2.7418	0.7764	-
9.9809	4720	-	-	-	0.7741

Framework Versions

• Python: 3.10.14
• Sentence Transformers: 3.3.1
• Transformers: 4.46.3
• PyTorch: 2.4.0
• Accelerate: 0.27.2
• Datasets: 3.1.0
• Tokenizers: 0.20.3

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}
}