SentenceTransformer based on izdastolga/checkpoint-18-Aixr-Turkish-QA

This is a sentence-transformers model finetuned from izdastolga/checkpoint-18-Aixr-Turkish-QA on the aixr-msmarco-rag-wikirag-gpt-soru_cevap-tur_hist_quad dataset. It maps sentences & paragraphs to a 1024-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: izdastolga/checkpoint-18-Aixr-Turkish-QA
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- aixr-msmarco-rag-wikirag-gpt-soru_cevap-tur_hist_quad

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: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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})
  (2): Normalize()
)

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("tizdas/checkpoint-1925-aixr-msmarco-rag-wikirag-gpt-soru_cevap-tur_hist_quad-0.8030")
# Run inference
sentences = [
    'Instruct: Given a Turkish search query, retrieve relevant passages written in Turkish that best answer the query.\nQuery: kiribati vanuatu nerede bulunur',
    "Kiribati'nin en doğudaki adaları, Hawaii'nin güneyindeki güney Line Adaları, Dünya'daki en gelişmiş zamana sahiptir: UTC + 14 saat. Kiribati 1979'da Birleşik Krallık'tan bağımsız oldu. Başkent ve şimdi en kalabalık bölge olan Güney Tarawa, bir dizi geçitle bağlı olan bir dizi adacıktan oluşur.",
    'Memlük Sultanı Mansur Seyfettin Kalaunun',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

aixr-msmarco-rag-wikirag-gpt-soru_cevap-tur_hist_quad

Dataset: aixr-msmarco-rag-wikirag-gpt-soru_cevap-tur_hist_quad at 5e80fa0
Size: 415,046 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 36 tokens
mean: 44.21 tokens
max: 131 tokens

min: 3 tokens
mean: 97.81 tokens
max: 512 tokens

	anchor	positive
type	string	string
details	min: 36 tokens mean: 44.21 tokens max: 131 tokens	min: 3 tokens mean: 97.81 tokens max: 512 tokens

Samples:

anchor	positive
`Instruct: Given a Turkish search query, retrieve relevant passages written in Turkish that best answer the query. Query: Bir balıkta kişi başına ne kadar balık kızartılır`	`Kişi başına yaklaşık 4 ons veya 113.4 gram çiğ balık kızartması gerekir. 0.25 pound'a eşdeğerdir.`
`Instruct: Given a Turkish search query, retrieve relevant passages written in Turkish that best answer the query. Query: İnsanın dış görünüşüne bakarak asla hüküm vermemeliyiz. Buna katılıyor musun, neden?`	`Dış görünüş tek başına bir şey ifade etmez ama fikir verir. Fakat dış görünüş çok yanıltıcı da olabilmektedir. Bu nedenle her zaman dış görünüşten ziyade insanların karakterlerine odaklanmak önemlidir.`
`Instruct: Given a Turkish search query, retrieve relevant passages written in Turkish that best answer the query. Query: Kürtçe nerede konuşulur`	`Kürtçe, Türkiye, Kürdistan, İran, Suriye, Irak, İran, Ermenistan, Gürcistan ve Azerbaycan'ı kapsayan ve Avrupa ve Amerika Birleşik Devletleri'ne yayılmış büyük bir diasporaya sahip büyük bir bölgede konuşulan yakın ilişkili dillerin sürekliliğinden oluşan bir makro dildir. Status.`

Loss: CachedMultipleNegativesRankingLoss with these parameters:

{
    "scale": 50,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 144
per_device_eval_batch_size: 144
learning_rate: 1e-06
weight_decay: 0.01
num_train_epochs: 1
warmup_ratio: 0.1
fp16: True
tf32: True
dataloader_num_workers: 4
prompts: {'anchor': 'Instruct: Given a Turkish search query, retrieve relevant passages written in Turkish that best answer the query.\nQuery: '}

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 144
per_device_eval_batch_size: 144
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: 1e-06
weight_decay: 0.01
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: True
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 4
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: None
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: {'anchor': 'Instruct: Given a Turkish search query, retrieve relevant passages written in Turkish that best answer the query.\nQuery: '}
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss
0.0997	275	0.33
0.1995	550	0.0964
0.2992	825	0.0862
0.3990	1100	0.081
0.4987	1375	0.0764
0.5985	1650	0.0745
0.6982	1925	0.0726

Framework Versions

Python: 3.11.11
Sentence Transformers: 3.4.1
Transformers: 4.49.0
PyTorch: 2.5.1+cu124
Accelerate: 1.3.0
Datasets: 2.21.0
Tokenizers: 0.21.0

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",
}

CachedMultipleNegativesRankingLoss

@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

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Safetensors

Model size

0.6B params

Tensor type

F32

Model tree for tizdas/checkpoint-1925-aixr-msmarco-rag-wikirag-gpt-soru_cevap-tur_hist_quad-0.8030

Base model

izdastolga/checkpoint-18-Aixr-Turkish-QA

Finetuned

(1)

this model