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col-ettin-encoder-150M-TR

Sentence Similarity
Safetensors
sentence-transformers
Turkish
PyLate
modernbert
ColBERT
feature-extraction
Generated from Trainer
dataset_size:910904
loss:Contrastive
text-embeddings-inference
Model card Files
xet
Community

PyLate model based on ozayezerceli/ettin-encoder-150M-TR

This is a PyLate model finetuned from ozayezerceli/ettin-encoder-150M-TR on the msmarco-tr dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.

Model Details

Model Description

  • Model Type: PyLate model
  • Base model: ozayezerceli/ettin-encoder-150M-TR
  • Document Length: 180 tokens
  • Query Length: 32 tokens
  • Output Dimensionality: 128 tokens
  • Similarity Function: MaxSim
  • Training Dataset:
  • Language: tr

Model Sources

Full Model Architecture 

ColBERT(
  (0): Transformer({'max_seq_length': 179, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
  (1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False})
)

Usage

First install the PyLate library:

pip install -U pylate

Retrieval

Use this model with PyLate to index and retrieve documents. The index uses FastPLAID for efficient similarity search.

Indexing documents

Load the ColBERT model and initialize the PLAID index, then encode and index your documents:

from pylate import indexes, models, retrieve

# Step 1: Load the ColBERT model
model = models.ColBERT(
    model_name_or_path="pylate_model_id",
)

# Step 2: Initialize the PLAID index
index = indexes.PLAID(
    index_folder="pylate-index",
    index_name="index",
    override=True,  # This overwrites the existing index if any
)

# Step 3: Encode the documents
documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]

documents_embeddings = model.encode(
    documents,
    batch_size=32,
    is_query=False,  # Ensure that it is set to False to indicate that these are documents, not queries
    show_progress_bar=True,
)

# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
index.add_documents(
    documents_ids=documents_ids,
    documents_embeddings=documents_embeddings,
)

Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:

# To load an index, simply instantiate it with the correct folder/name and without overriding it
index = indexes.PLAID(
    index_folder="pylate-index",
    index_name="index",
)

Retrieving top-k documents for queries

Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries. To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:

# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)

# Step 2: Encode the queries
queries_embeddings = model.encode(
    ["query for document 3", "query for document 1"],
    batch_size=32,
    is_query=True,  #  # Ensure that it is set to False to indicate that these are queries
    show_progress_bar=True,
)

# Step 3: Retrieve top-k documents
scores = retriever.retrieve(
    queries_embeddings=queries_embeddings,
    k=10,  # Retrieve the top 10 matches for each query
)

Reranking

If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:

from pylate import rank, models

queries = [
    "query A",
    "query B",
]

documents = [
    ["document A", "document B"],
    ["document 1", "document C", "document B"],
]

documents_ids = [
    [1, 2],
    [1, 3, 2],
]

model = models.ColBERT(
    model_name_or_path="pylate_model_id",
)

queries_embeddings = model.encode(
    queries,
    is_query=True,
)

documents_embeddings = model.encode(
    documents,
    is_query=False,
)

reranked_documents = rank.rerank(
    documents_ids=documents_ids,
    queries_embeddings=queries_embeddings,
    documents_embeddings=documents_embeddings,
)

Training Details

Training Dataset

msmarco-tr

  • Dataset: msmarco-tr at ffad30a
  • Size: 910,904 training 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: 17.07 tokens
    • max: 32 tokens
    • min: 25 tokens
    • mean: 31.99 tokens
    • max: 32 tokens
    • min: 21 tokens
    • mean: 31.99 tokens
    • max: 32 tokens
  • Samples:
    query positive negative
    sinir dokusundaki miyelin kılıfı nerede Miyelin, bir tabaka oluşturan akson dielektrik (elektriksel olarak yalıtkan) malzemeyi çevreleyen yağlı bir beyaz maddedir, miyelin kılıfı, genellikle sadece bir nöronun aksonu etrafında bulunur. Sinir sisteminin düzgün çalışması için gereklidir. Bir tür glial hücrenin bir dış büyümesidir. Miyelin kılıfının üretimi miyelinasyon olarak adlandırılır. İnsanlarda, miyelin kılıfı 14'üncü haftada başlar. İnsanlarda, dört temel doku tipi vardır: epitel dokusu, bağ dokusu, kas dokusu ve sinir dokusu. Her genel doku tipi içinde, belirli doku tipleri vardır. Bunu bir futbol takımı gibi düşünün.Her biri sahada kendi 'iş' olan bireysel oyuncular vardır.n insanlar, dört temel doku tipi vardır: epitel dokusu, bağ dokusu, kas dokusu ve sinir dokusu. Bu genel doku tipinde, her bir genel doku tipinde vardır.
    Okulların Makine Mühendisliğini Sundukları Şeyler Makine Mühendisliği Teknolojisi Dereceleri için Üst Okullar. Pennsylvania Eyalet Üniversitesi - Harrisburg, Purdue Üniversitesi ve Houston Üniversitesi, makine mühendisliği teknolojisi (MET) alanında lisans derecesi sunan üç okuldur. Bu üniversitelerdeki MET programları hakkında daha fazla bilgi edinmek için okumaya devam edin. Mühendis tanımı, motorların veya makinelerin tasarımında, yapımında ve kullanımında veya çeşitli mühendislik dallarından herhangi birinde eğitimli ve yetenekli bir kişi: bir makine mühendisi; bir inşaat mühendisi. Daha fazla bilgi için bkz.
    kim navigatör karıştırma valfleri taşır BRADLEY THERMOSTATIC MIXING VANAS. Bradley Corporation, armatür ve sıhhi tesisat ürünlerinin üretiminde lider, dört hat üretir. termostatik karıştırma valfleri (TMVs). Bradley Navigator Yüksek Düşük termostatik karıştırma valfleri vardır. Dıştan gelen talebin çok düşük olduğu uygulamalar için idealdir. Hidrolik Valfler. Eaton valfleri, tüm dünyadaki pazarlarda müşterilerimiz için rekabet avantajı sağlar. Geniş bir seçenek yelpazesinde benzersiz kalite sunan yüksek değerli hidrolik valf ürünlerimiz, gerçek endüstri liderlerinin tüm özelliklerini ve performans seviyelerini içerir. Endüstriyel Valfler.
  • Loss: pylate.losses.contrastive.Contrastive

Evaluation Dataset

msmarco-tr

  • Dataset: msmarco-tr at ffad30a
  • Size: 9,202 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: 16.9 tokens
    • max: 32 tokens
    • min: 29 tokens
    • mean: 32.0 tokens
    • max: 32 tokens
    • min: 28 tokens
    • mean: 31.99 tokens
    • max: 32 tokens
  • Samples:
    query positive negative
    Ermin hangi hayvandır 1 Aslında ermine kelimesi beyaz kürklü bir hayvanı ifade ederken, sırt üstü kahverengi kürklü ve karnında baş ve beyaz kürklü bireyler için stoat kullanılır. Dünyada kaç hayvan türü var? İşte kaba bir sayım ve bilim adamlarının sayılara nasıl ulaştıklarına dair kısa bir açıklama. Dünyada kaç hayvan türü var? İşte kaba bir sayım ve bilim adamlarının sayılara nasıl ulaştıklarına dair kısa bir açıklama. Kaç hayvan türü var? https://www.thoughtco.com/how-many-animal-türleri-on-planet-130923 Strauss, Bob.
    Abacus nereden çıktı Abacus: Kısa Bir Tarih. Abacus, kökeni Yunanca abax veya abakon (masa veya tablet anlamına gelir) kelimelerinden gelen ve muhtemelen kum anlamına gelen Semitik abq kelimesinden kaynaklanan Latince bir kelimedir. Abacus, büyük sayıları saymak için kullanılan birçok sayma cihazından biridir. Hücre apeksinde, bir flagellum için çapa alanı olan bazal gövdedir. Bazal cisimler, dokuz periferik mikrotübül üçlüsü ile centrioles'inkine benzer bir alt yapıya sahiptir (görüntünün alt merkezindeki yapıya bakınız).
    Başın arkasında radyasyon tedavisi yüz kızarıklığına neden olur mu Radyasyon Terapisinin En Yaygın Yan Etkileri. Cilt reaksiyonu: Radyasyon tedavisinin yaygın bir yan etkisi, tedavi edilen vücut bölgesinde cilt tahrişidir. Cilt reaksiyonu, hafif kızarıklık ve kuruluktan (güneş yanığına benzer) bazı hastalarda cildin şiddetli soyulmasına (desquamation) kadar değişebilir. Bu açıklama amfizemi işaret edebilir. Bu, sigara içme geçmişiniz varsa daha da muhtemeldir. Radyasyon terapisi bilinen nedenlerden biri değildir. Bu konuda daha fazla cevap almak ve semptomlarınızı çözmeye yardımcı olmak için bir pulmonologla takip etmenizi isteyeceğim. Umarım bu, sorgunuzu tamamen ele alır. Sigara içme geçmişiniz varsa, daha da fazla umut eder. Radyasyon terapisi, bu sorunun çözümüne yardımcı olmanızı ve bu sorunun cevabını takip etmenizi isterim.
  • Loss: pylate.losses.contrastive.Contrastive

Training Hyperparameters

Non-Default Hyperparameters

  • per_device_train_batch_size: 32
  • per_device_eval_batch_size: 32
  • learning_rate: 3e-06
  • num_train_epochs: 1
  • fp16: True

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: no
  • prediction_loss_only: True
  • per_device_train_batch_size: 32
  • per_device_eval_batch_size: 32
  • 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: 3e-06
  • 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: 0.0
  • 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}
  • 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}
  • parallelism_config: None
  • deepspeed: None
  • label_smoothing_factor: 0.0
  • optim: adamw_torch_fused
  • 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
  • hub_revision: None
  • 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
  • liger_kernel_config: None
  • eval_use_gather_object: False
  • average_tokens_across_devices: False
  • prompts: None
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: proportional
  • router_mapping: {}
  • learning_rate_mapping: {}

Training Logs

Click to expand
Epoch Step Training Loss
0.0000 1 4.1579
0.0035 100 3.3423
0.0070 200 1.9776
0.0105 300 1.3823
0.0141 400 1.1908
0.0176 500 1.0101
0.0211 600 0.9372
0.0246 700 0.8878
0.0281 800 0.8133
0.0316 900 0.8001
0.0351 1000 0.7634
0.0386 1100 0.7144
0.0422 1200 0.7239
0.0457 1300 0.7352
0.0492 1400 0.7019
0.0527 1500 0.7021
0.0562 1600 0.6415
0.0597 1700 0.6469
0.0632 1800 0.6384
0.0667 1900 0.5887
0.0703 2000 0.6121
0.0738 2100 0.6309
0.0773 2200 0.5957
0.0808 2300 0.5758
0.0843 2400 0.6137
0.0878 2500 0.606
0.0913 2600 0.5516
0.0948 2700 0.6184
0.0984 2800 0.5571
0.1019 2900 0.5462
0.1054 3000 0.62
0.1089 3100 0.5548
0.1124 3200 0.564
0.1159 3300 0.5299
0.1194 3400 0.5464
0.1230 3500 0.5554
0.1265 3600 0.5818
0.1300 3700 0.5608
0.1335 3800 0.5641
0.1370 3900 0.5337
0.1405 4000 0.5381
0.1440 4100 0.5308
0.1475 4200 0.5036
0.1511 4300 0.5292
0.1546 4400 0.5007
0.1581 4500 0.5442
0.1616 4600 0.5201
0.1651 4700 0.5331
0.1686 4800 0.4986
0.1721 4900 0.5312
0.1756 5000 0.4578
0.1792 5100 0.5328
0.1827 5200 0.488
0.1862 5300 0.5007
0.1897 5400 0.4652
0.1932 5500 0.4924
0.1967 5600 0.5278
0.2002 5700 0.4527
0.2038 5800 0.4902
0.2073 5900 0.4796
0.2108 6000 0.4582
0.2143 6100 0.491
0.2178 6200 0.4789
0.2213 6300 0.4728
0.2248 6400 0.4737
0.2283 6500 0.4565
0.2319 6600 0.4512
0.2354 6700 0.4813
0.2389 6800 0.4638
0.2424 6900 0.5149
0.2459 7000 0.4587
0.2494 7100 0.4805
0.2529 7200 0.4847
0.2564 7300 0.5415
0.2600 7400 0.5055
0.2635 7500 0.4437
0.2670 7600 0.4712
0.2705 7700 0.4736
0.2740 7800 0.4242
0.2775 7900 0.4598
0.2810 8000 0.4398
0.2845 8100 0.4577
0.2881 8200 0.4501
0.2916 8300 0.4757
0.2951 8400 0.4386
0.2986 8500 0.4636
0.3021 8600 0.4282
0.3056 8700 0.44
0.3091 8800 0.4413
0.3127 8900 0.4548
0.3162 9000 0.4515
0.3197 9100 0.461
0.3232 9200 0.4299
0.3267 9300 0.4161
0.3302 9400 0.4567
0.3337 9500 0.4036
0.3372 9600 0.3965
0.3408 9700 0.4746
0.3443 9800 0.4078
0.3478 9900 0.4234
0.3513 10000 0.4423
0.3548 10100 0.4151
0.3583 10200 0.4298
0.3618 10300 0.4246
0.3653 10400 0.4292
0.3689 10500 0.4258
0.3724 10600 0.4153
0.3759 10700 0.4239
0.3794 10800 0.4286
0.3829 10900 0.4364
0.3864 11000 0.4276
0.3899 11100 0.3974
0.3935 11200 0.4334
0.3970 11300 0.4343
0.4005 11400 0.4039
0.4040 11500 0.4148
0.4075 11600 0.4022
0.4110 11700 0.4203
0.4145 11800 0.4188
0.4180 11900 0.4265
0.4216 12000 0.4125
0.4251 12100 0.3977
0.4286 12200 0.3811
0.4321 12300 0.4199
0.4356 12400 0.4062
0.4391 12500 0.3842
0.4426 12600 0.4063
0.4461 12700 0.4069
0.4497 12800 0.3984
0.4532 12900 0.4161
0.4567 13000 0.3827
0.4602 13100 0.4201
0.4637 13200 0.3808
0.4672 13300 0.4335
0.4707 13400 0.3975
0.4742 13500 0.4059
0.4778 13600 0.4077
0.4813 13700 0.4164
0.4848 13800 0.4146
0.4883 13900 0.3857
0.4918 14000 0.3952
0.4953 14100 0.416
0.4988 14200 0.413
0.5024 14300 0.403
0.5059 14400 0.4139
0.5094 14500 0.4237
0.5129 14600 0.3944
0.5164 14700 0.4237
0.5199 14800 0.357
0.5234 14900 0.3983
0.5269 15000 0.4182
0.5305 15100 0.4478
0.5340 15200 0.3948
0.5375 15300 0.4127
0.5410 15400 0.4161
0.5445 15500 0.3815
0.5480 15600 0.3875
0.5515 15700 0.3859
0.5550 15800 0.399
0.5586 15900 0.3936
0.5621 16000 0.3927
0.5656 16100 0.3961
0.5691 16200 0.3657
0.5726 16300 0.3743
0.5761 16400 0.3881
0.5796 16500 0.3815
0.5832 16600 0.3719
0.5867 16700 0.365
0.5902 16800 0.4117
0.5937 16900 0.3819
0.5972 17000 0.3821
0.6007 17100 0.3589
0.6042 17200 0.367
0.6077 17300 0.3768
0.6113 17400 0.3652
0.6148 17500 0.3766
0.6183 17600 0.3952
0.6218 17700 0.3786
0.6253 17800 0.3975
0.6288 17900 0.3944
0.6323 18000 0.3884
0.6358 18100 0.3664
0.6394 18200 0.3755
0.6429 18300 0.3692
0.6464 18400 0.3747
0.6499 18500 0.3646
0.6534 18600 0.3856
0.6569 18700 0.3698
0.6604 18800 0.3872
0.6639 18900 0.3801
0.6675 19000 0.3736
0.6710 19100 0.4025
0.6745 19200 0.3689
0.6780 19300 0.39
0.6815 19400 0.4083
0.6850 19500 0.3403
0.6885 19600 0.332
0.6921 19700 0.3585
0.6956 19800 0.4102
0.6991 19900 0.3708
0.7026 20000 0.4117
0.7061 20100 0.3862
0.7096 20200 0.3819
0.7131 20300 0.3653
0.7166 20400 0.3518
0.7202 20500 0.3804
0.7237 20600 0.3539
0.7272 20700 0.3782
0.7307 20800 0.3846
0.7342 20900 0.3412
0.7377 21000 0.38
0.7412 21100 0.3799
0.7447 21200 0.368
0.7483 21300 0.3733
0.7518 21400 0.3979
0.7553 21500 0.3677
0.7588 21600 0.362
0.7623 21700 0.3897
0.7658 21800 0.3481
0.7693 21900 0.39
0.7729 22000 0.366
0.7764 22100 0.4224
0.7799 22200 0.3745
0.7834 22300 0.3616
0.7869 22400 0.3855
0.7904 22500 0.3742
0.7939 22600 0.3605
0.7974 22700 0.3467
0.8010 22800 0.3614
0.8045 22900 0.3484
0.8080 23000 0.3769
0.8115 23100 0.3324
0.8150 23200 0.3276
0.8185 23300 0.3684
0.8220 23400 0.3595
0.8255 23500 0.3884
0.8291 23600 0.3586
0.8326 23700 0.3614
0.8361 23800 0.3505
0.8396 23900 0.3665
0.8431 24000 0.3771
0.8466 24100 0.3935
0.8501 24200 0.3489
0.8536 24300 0.3406
0.8572 24400 0.3901
0.8607 24500 0.3444
0.8642 24600 0.3873
0.8677 24700 0.3668
0.8712 24800 0.3452
0.8747 24900 0.3497
0.8782 25000 0.3314
0.8818 25100 0.3878
0.8853 25200 0.3705
0.8888 25300 0.3564
0.8923 25400 0.3826
0.8958 25500 0.3674
0.8993 25600 0.3591
0.9028 25700 0.3525
0.9063 25800 0.3603
0.9099 25900 0.3439
0.9134 26000 0.3636
0.9169 26100 0.3552
0.9204 26200 0.3697
0.9239 26300 0.3957
0.9274 26400 0.373
0.9309 26500 0.3878
0.9344 26600 0.3868
0.9380 26700 0.3648
0.9415 26800 0.3399
0.9450 26900 0.344
0.9485 27000 0.3834
0.9520 27100 0.3623
0.9555 27200 0.3464
0.9590 27300 0.3666
0.9626 27400 0.3657
0.9661 27500 0.3495
0.9696 27600 0.3527
0.9731 27700 0.3484
0.9766 27800 0.362
0.9801 27900 0.3879
0.9836 28000 0.3558
0.9871 28100 0.3666
0.9907 28200 0.3456
0.9942 28300 0.3358
0.9977 28400 0.342

Framework Versions

  • Python: 3.12.12
  • Sentence Transformers: 5.1.1
  • PyLate: 1.3.4
  • Transformers: 4.56.2
  • PyTorch: 2.8.0+cu126
  • Accelerate: 1.10.1
  • Datasets: 4.0.0
  • Tokenizers: 0.22.1

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"
}

PyLate 

@misc{PyLate,
title={PyLate: Flexible Training and Retrieval for Late Interaction Models},
author={Chaffin, Antoine and Sourty, Raphaël},
url={https://github.com/lightonai/pylate},
year={2024}
}
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