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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1535
- loss:MultipleNegativesRankingLoss
base_model: nomic-ai/nomic-embed-text-v2-moe
widget:
- source_sentence: İhale Yönetmeliği Madde 19'a göre doğrudan temin için mal/hizmet/tarife/seyahat
alımlarının bedel sınırı nedir?
sentences:
- 'Week9 (Nov 25): Condition Variables, Deadlocks / Project Phase II: Scheduler
- @Nov 28, Preliminary Report Due: Dec 23, How to write Project Report, Phase
1- DEMO'
- ç) Üniversitenin tahmini bedeli bir önceki hesap dönemi toplam giderlerinin TÜFE
oranına göre güncellenecek üçyüzsekizbinüçyüzyetmişdört TL’ye karşılık gelen tutardaki
bedeli aşmayacak olan mal ve hizmet alımları, tarifeli alımlar ile seyahat alımları.
- MADDE 9 – (1) Yabancı uyruklu öğrenciler hakkında ilgili mevzuat hükümleri ile
Senato tarafından belirlenen esaslar uygulanır.
- source_sentence: Madde 8'e göre Komisyon Başkanı kime karşı sorumludur?
sentences:
- Komisyon Başkanı, Rektöre karşı sorumludur.
- Komisyon Başkanı, çalışma birimlerinin faaliyetlerini izler ve denetler.
- Alınacak pedagojik formasyon dersleri öğrencinin dönemlik ders yükünün üzerinde
olması halinde genel not ortalaması 3.00 ve üzeri olan öğrencilerden ücret alınmayacak...
- source_sentence: What is the grading breakdown for CSE 462?
sentences:
- 'Grading Breakdown: 30% Midterm
45% Final
25% Assignments'
- Teklifler iadeli taahhütlü olarak da gönderilebilir.
- Any form of cheating will be reported to the faculty's relevant administrative
body for further action.
- source_sentence: Resmi Yazışma Yönergesi Madde 16'ya göre paragraflar nasıl başlar
ve hizalanır?
sentences:
- (8)Yazışma birim kodu olmayan veya verilmeyen hiçbir birim yazışma yapamaz.
- (3) Zorunlu hâllerde veya olağanüstü durumlarda hazırlanan olur yazılarında “OLUR”
ibaresinden sonra tarih ve imza için uygun boş satır bırakılarak ilk satırda imzalayanın
adı ve soyadına, ikinci satırda ise unvan bilgilerine yer verilir (Örnek 18).
- (4) Paragrafa 1,25 cm içeriden başlanır ve metin iki yana hizalanır.
- source_sentence: CSE 447 (Ozkaya) sınavında kopya çekme girişimi nasıl değerlendirilir?
sentences:
- 'Week-10 AVL tree
Week-11 IPR tree
Week-12 B tree
Week-13 B+ tree'
- 'Alan Eğitimcisi : Doktorasını ve/veya doçentliğini, ilgili alan eğitiminde (fizik
eğitimi, kimya eğitimi, biyoloji eğitimi, matematik eğitimi, tarih eğitimi, din
eğitimi, Türkçe eğitimi vb.) almış öğretim üyesini,'
- 'Exam Cheating Policy: Any attempt at cheating during the midterm and final exams
will be treated seriously.'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on nomic-ai/nomic-embed-text-v2-moe
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v2-moe](https://huggingface.co/nomic-ai/nomic-embed-text-v2-moe) on the json 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:** [nomic-ai/nomic-embed-text-v2-moe](https://huggingface.co/nomic-ai/nomic-embed-text-v2-moe) <!-- at revision 1066b6599d099fbb93dfcb64f9c37a7c9e503e85 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- json
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NomicBertModel
(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})
(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("Demircan12/nomic-embed-text-v2-moe-YeditepeFT")
# Run inference
sentences = [
'CSE 447 (Ozkaya) sınavında kopya çekme girişimi nasıl değerlendirilir?',
'Exam Cheating Policy: Any attempt at cheating during the midterm and final exams will be treated seriously.',
'Week-10 AVL tree\nWeek-11 IPR tree\nWeek-12 B tree\nWeek-13 B+ tree',
]
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]
```
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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
#### json
* Dataset: json
* Size: 1,535 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: 11 tokens</li><li>mean: 22.23 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 38.09 tokens</li><li>max: 247 tokens</li></ul> |
* Samples:
| anchor | positive |
|:--------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Are the Fall (Regular) and Spring (Irregular) programs in the Faculty of Law different according to MADDE 5?</code> | <code>Güz (Regular) ve Bahar (Irregular) programları başlangıç zamanı dışında her açıdan birbirine denktir.</code> |
| <code>According to MADDE 6, who can take the Postgraduate Proficiency Exam?</code> | <code>(2) Yeterlik Sınavı’na, yeni kayıtlı öğrencilerle birlikte halen hazırlık programında öğrenimine devam eden öğrenciler de girebilirler.</code> |
| <code>What is the purpose of the Horizontal/Vertical Transfer Adaptation Principles (Madde 1)?</code> | <code>Madde 1- (1) Yatay/Dikey Geçiş İntibak Esaslarının amacı, Yeditepe Üniversitesine yatay geçiş veya dikey geçiş ile kabul edilen öğrencilerin intibak işlemlerine ilişkin esas ve usulleri belirlemektir.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### json
* Dataset: json
* Size: 220 evaluation samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 220 samples:
| | anchor | positive |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 12 tokens</li><li>mean: 21.82 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 36.3 tokens</li><li>max: 160 tokens</li></ul> |
* Samples:
| anchor | positive |
|:----------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>CSE 439 dersinin notlandırma dağılımı nasıldır?</code> | <code>Grading Breakdown: Midterm: 30%\nFinal: 35%\nHomeworks and Quizzes: 15%\nTerm Project: 20%</code> |
| <code>Hukuk Fakültesi Yönetmeliği Madde 13'e göre dersler öğrencilerin hangi yeteneklerini geliştirmeye yönelik yürütülür?</code> | <code>(3) Dersler öğrencilerin muhakeme ve sözlü-yazılı anlatım yeteneklerinin geliştirilmesine katkı sağlayacak şekilde yürütülür.</code> |
| <code>Yönetmelik Madde 18'e göre hangi sınav Yönetmeliğine göre başarılı olanlar dil sınavından muaf tutulur?</code> | <code>b) Öğretim dilinin anadil olarak konuşulduğu ülkelerde yabancıların yükseköğrenim görebilmeleri için aranan asgari yabancı dil seviyesinin tespiti amacına yönelik olarak yapılan sınavlarda ve 25/9/2013 tarihli ve 28776 sayılı Resmî Gazete’de yayımlanan Yeditepe Üniversitesi Yabancı Diller Hazırlık Programı Eğitim-Öğretim ve Sınav Yönetmeliği hükümlerine göre başarılı olanlar.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"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
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `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`: 5e-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`: 5
- `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`: False
- `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}
- `tp_size`: 0
- `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
- `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
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 1.0417 | 100 | 0.1199 | 0.0659 |
| 2.0833 | 200 | 0.0236 | 0.0524 |
| 3.125 | 300 | 0.0145 | 0.0578 |
| 4.1667 | 400 | 0.0102 | 0.0617 |
### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## 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",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@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}
}
```
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