mertcobanov/mpnet-base-all-nli-triplet-turkish-v2

MPNet base trained on AllNLI-turkish triplets

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
• Language: en
• 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: 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-v2")
# 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	0.7454	0.7525

Training Details

Training Dataset

all-nli-triplets-turkish

• Dataset: all-nli-triplets-turkish at bff203b
• Size: 13,842 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: 8 tokens mean: 13.42 tokens max: 95 tokens	min: 8 tokens mean: 31.64 tokens max: 93 tokens	min: 6 tokens mean: 32.03 tokens max: 89 tokens

• Samples:

  anchor_translated	positive_translated	negative_translated
  Asyalı okul çocukları birbirlerinin omuzlarında oturuyor.	Okul çocukları bir arada	Asyalı fabrika işçileri oturuyor.
  İnsanlar dışarıda.	Arka planda resmi kıyafetler giymiş bir grup insan var ve beyaz gömlekli, haki pantolonlu bir adam toprak yoldan yeşil çimenlere atlıyor.	Bir odada üç kişiyle birlikte büyük bir kamera tutan bir adam.
  Bir adam dışarıda.	Adam yarış sırasında yan sepetten bir su birikintisine düşer.	Beyaz bir sarık sarmış gömleksiz bir adam bir ağaç gövdesine tırmanıyor.

• Loss: MultipleNegativesRankingLoss with these parameters:

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

Evaluation Dataset

all-nli-triplets-turkish

• Dataset: all-nli-triplets-turkish at bff203b
• 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: 5 tokens mean: 42.62 tokens max: 192 tokens	min: 5 tokens mean: 22.58 tokens max: 77 tokens	min: 5 tokens mean: 22.07 tokens max: 65 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: 16
• per_device_eval_batch_size: 16
• learning_rate: 2e-05
• num_train_epochs: 1
• 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: 16
• per_device_eval_batch_size: 16
• 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: 1
• 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	-	-	0.6092	-
0.1155	100	2.7414	1.6615	0.7429	-
0.2309	200	1.64	1.4650	0.7483	-
0.3464	300	1.2391	1.4068	0.7561	-
0.4619	400	1.1146	1.4367	0.7549	-
0.5774	500	1.0341	1.4887	0.7486	-
0.6928	600	0.7568	1.4568	0.7535	-
0.8083	700	0.7216	1.5680	0.7451	-
0.9238	800	0.5919	1.5492	0.7454	-
1.0	866	-	-	-	0.7525

Framework Versions

• Python: 3.10.14
• Sentence Transformers: 3.3.1
• Transformers: 4.46.3
• PyTorch: 2.3.0
• Accelerate: 1.1.1
• 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}
}