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

sourceHugging Faceupdated 2y agoView on Hugging Face

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Model Card

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:

bash

pip install -U sentence-transformers

Then you can load this model and run inference.

python

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]

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Downstream Usage (Sentence Transformers)

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Training Details

Training Dataset

aixr-msmarco-rag-wikirag-gpt-sorucevap-turhist_quad

—Dataset: aixr-msmarco-rag-wikirag-gpt-soru_cevap-tur_hist_quad at 5e80fa0
—Size: 415,046 training samples
—Columns: <code>anchor</code> and <code>positive</code>
—Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 36 tokens</li><li>mean: 44.21 tokens</li><li>max: 131 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 97.81 tokens</li><li>max: 512 tokens</li></ul> |
—Samples: | anchor | positive | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Instruct: Given a Turkish search query, retrieve relevant passages written in Turkish that best answer the query.<br>Query: Bir balıkta kişi başına ne kadar balık kızartılır</code> | <code>Kişi başına yaklaşık 4 ons veya 113.4 gram çiğ balık kızartması gerekir. 0.25 pound'a eşdeğerdir.</code> | | <code>Instruct: Given a Turkish search query, retrieve relevant passages written in Turkish that best answer the query.<br>Query: İnsanın dış görünüşüne bakarak asla hüküm vermemeliyiz. Buna katılıyor musun, neden?</code> | <code>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.</code> | | <code>Instruct: Given a Turkish search query, retrieve relevant passages written in Turkish that best answer the query.<br>Query: Kürtçe nerede konuşulur</code> | <code>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.</code> |
—Loss: <code>CachedMultipleNegativesRankingLoss</code> with these parameters:

json

  {
      "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

<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: 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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
—fsdp_transformer_layer_cls_to_wrap: None
—accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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

</details>

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

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",
}

CachedMultipleNegativesRankingLoss

bibtex

@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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