ColmmBERT-base-TR / README.md

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	---
	language:
	- tr
	tags:
	- ColBERT
	- PyLate
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:910904
	- loss:Contrastive
	base_model: ozayezerceli/mmBERT-base-TR
	datasets:
	- parsak/msmarco-tr
	pipeline_tag: sentence-similarity
	library_name: PyLate
	---

	# PyLate model based on ozayezerceli/mmBERT-base-TR

	This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [ozayezerceli/mmBERT-base-TR](https://huggingface.co/ozayezerceli/mmBERT-base-TR) on the [msmarco-tr](https://huggingface.co/datasets/parsak/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/mmBERT-base-TR](https://huggingface.co/ozayezerceli/mmBERT-base-TR) <!-- at revision d2c45c86ca983e13f7f14d74b5046930232d205d -->
	- Document Length: 180 tokens
	- Query Length: 32 tokens
	- Output Dimensionality: 128 tokens
	- Similarity Function: MaxSim
	- Training Dataset:
	- [msmarco-tr](https://huggingface.co/datasets/parsak/msmarco-tr)
	- Language: tr
	<!-- - License: Unknown -->

	### Model Sources

	- Documentation: [PyLate Documentation](https://lightonai.github.io/pylate/)
	- Repository: [PyLate on GitHub](https://github.com/lightonai/pylate)
	- Hugging Face: [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate)

	### 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:

	```bash
	pip install -U pylate
	```

	### Retrieval

	Use this model with PyLate to index and retrieve documents. The index uses [FastPLAID](https://github.com/lightonai/fast-plaid) for efficient similarity search.

	#### Indexing documents

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

	```python
	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:

	```python
	# 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:

	```python
	# 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:

	```python
	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,
	)
	```

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	<details><summary>Click to see the direct usage in Transformers</summary>

	</details>
	-->

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	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

	</details>
	-->

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	## Training Details

	### Training Dataset

	#### msmarco-tr

	* Dataset: [msmarco-tr](https://huggingface.co/datasets/parsak/msmarco-tr) at [ffad30a](https://huggingface.co/datasets/parsak/msmarco-tr/tree/ffad30a7b0648f1c789c639db6c1d4720c22274c)
	* Size: 910,904 training samples
	* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| positive \| negative \|
	\|:--------\|:----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 5 tokens</li><li>mean: 12.04 tokens</li><li>max: 32 tokens</li></ul> \| <ul><li>min: 24 tokens</li><li>mean: 31.98 tokens</li><li>max: 32 tokens</li></ul> \| <ul><li>min: 25 tokens</li><li>mean: 31.98 tokens</li><li>max: 32 tokens</li></ul> \|
	* Samples:
	\| query \| positive \| negative \|
	\|:---------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>sinir dokusundaki miyelin kılıfı nerede</code> \| <code>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.</code> \| <code>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.</code> \|
	\| <code>Okulların Makine Mühendisliğini Sundukları Şeyler</code> \| <code>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.</code> \| <code>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.</code> \|
	\| <code>kim navigatör karıştırma valfleri taşır</code> \| <code>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.</code> \| <code>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.</code> \|
	* Loss: <code>pylate.losses.contrastive.Contrastive</code>

	### Evaluation Dataset

	#### msmarco-tr

	* Dataset: [msmarco-tr](https://huggingface.co/datasets/parsak/msmarco-tr) at [ffad30a](https://huggingface.co/datasets/parsak/msmarco-tr/tree/ffad30a7b0648f1c789c639db6c1d4720c22274c)
	* Size: 9,202 evaluation samples
	* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| positive \| negative \|
	\|:--------\|:----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 5 tokens</li><li>mean: 11.96 tokens</li><li>max: 32 tokens</li></ul> \| <ul><li>min: 25 tokens</li><li>mean: 31.98 tokens</li><li>max: 32 tokens</li></ul> \| <ul><li>min: 23 tokens</li><li>mean: 31.96 tokens</li><li>max: 32 tokens</li></ul> \|
	* Samples:
	\| query \| positive \| negative \|
	\|:--------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>Ermin hangi hayvandır</code> \| <code>1 Aslında ermine kelimesi beyaz kürklü bir hayvanı ifade ederken, sırt üstü kahverengi kürklü ve karnında baş ve beyaz kürklü bireyler için stoat kullanılır.</code> \| <code>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.</code> \|
	\| <code>Abacus nereden çıktı</code> \| <code>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.</code> \| <code>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).</code> \|
	\| <code>Başın arkasında radyasyon tedavisi yüz kızarıklığına neden olur mu</code> \| <code>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.</code> \| <code>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.</code> \|
	* Loss: <code>pylate.losses.contrastive.Contrastive</code>

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `per_device_train_batch_size`: 64
	- `per_device_eval_batch_size`: 64
	- `learning_rate`: 3e-06
	- `num_train_epochs`: 1
	- `fp16`: True

	#### All Hyperparameters
	<details><summary>Click to expand</summary>

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: no
	- `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`: 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`: {}

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \|
	\|:------:\|:-----:\|:-------------:\|
	\| 0.0001 \| 1 \| 4.262 \|
	\| 0.0351 \| 500 \| 0.8248 \|
	\| 0.0703 \| 1000 \| 0.3406 \|
	\| 0.1054 \| 1500 \| 0.2613 \|
	\| 0.1405 \| 2000 \| 0.2255 \|
	\| 0.1756 \| 2500 \| 0.1987 \|
	\| 0.2108 \| 3000 \| 0.1839 \|
	\| 0.2459 \| 3500 \| 0.1748 \|
	\| 0.2810 \| 4000 \| 0.1781 \|
	\| 0.3162 \| 4500 \| 0.1596 \|
	\| 0.3513 \| 5000 \| 0.1542 \|
	\| 0.3864 \| 5500 \| 0.1505 \|
	\| 0.4216 \| 6000 \| 0.1457 \|
	\| 0.4567 \| 6500 \| 0.1361 \|
	\| 0.4918 \| 7000 \| 0.1372 \|
	\| 0.5269 \| 7500 \| 0.1371 \|
	\| 0.5621 \| 8000 \| 0.1384 \|
	\| 0.5972 \| 8500 \| 0.1319 \|
	\| 0.6323 \| 9000 \| 0.132 \|
	\| 0.6675 \| 9500 \| 0.1268 \|
	\| 0.7026 \| 10000 \| 0.1265 \|
	\| 0.7377 \| 10500 \| 0.1236 \|
	\| 0.7729 \| 11000 \| 0.1256 \|
	\| 0.8080 \| 11500 \| 0.1225 \|
	\| 0.8431 \| 12000 \| 0.1221 \|
	\| 0.8782 \| 12500 \| 0.1177 \|
	\| 0.9134 \| 13000 \| 0.1218 \|
	\| 0.9485 \| 13500 \| 0.1215 \|
	\| 0.9836 \| 14000 \| 0.1201 \|


	### Framework Versions
	- Python: 3.12.12
	- Sentence Transformers: 5.1.1
	- PyLate: 1.3.4
	- Transformers: 4.56.2
	- PyTorch: 2.8.0+cu128
	- Accelerate: 1.10.1
	- Datasets: 4.0.0
	- Tokenizers: 0.22.1


	## 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"
	}
	```

	#### PyLate
	```bibtex
	@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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