sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:38739 loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:1705.00652 base_model:sentence-transformers/paraphrase-xlm-r-multilingual-v1 base_model:finetune:sentence-transformers/paraphrase-xlm-r-multilingual-v1 autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/paraphrase-xlm-r-multilingual-v1
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:38739
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: '''Turks ve Caicos Adaları''ndaki Afrikalıların nüfusu nedir?'
  sentences:
  - "CREATE TABLEethnicGroup (\n    Country TEXT,\n    Name TEXT PRIMARY KEY,\n  \
    \  Percentage REAL,\n    FOREIGN KEY (Country) REFERENCES country(None)\n);"
  - "CREATE TABLEPatient (\n    ID INTEGER PRIMARY KEY,\n    SEX TEXT,\n    Birthday\
    \ DATE,\n    Description DATE,\n    First Date DATE,\n    Admission TEXT,\n  \
    \  Diagnosis TEXT\n);"
  - "CREATE TABLEwrites (\n    paperId INTEGER PRIMARY KEY,\n    authorId INTEGER,\n\
    \    FOREIGN KEY (authorId) REFERENCES author(authorId),\n    FOREIGN KEY (paperId)\
    \ REFERENCES paper(paperId)\n);"
- source_sentence: Teksas'ın başkenti nedir
  sentences:
  - "CREATE TABLEprofessor (\n    EMP_NUM INT,\n    DEPT_CODE varchar(10),\n    PROF_OFFICE\
    \ varchar(50),\n    PROF_EXTENSION varchar(4),\n    PROF_HIGH_DEGREE varchar(5),\n\
    \    FOREIGN KEY (DEPT_CODE) REFERENCES DEPARTMENT(DEPT_CODE),\n    FOREIGN KEY\
    \ (EMP_NUM) REFERENCES EMPLOYEE(EMP_NUM)\n);"
  - "CREATE TABLEBusiness_Hours (\n    business_id INTEGER PRIMARY KEY,\n    day_id\
    \ INTEGER,\n    opening_time TEXT,\n    closing_time TEXT,\n    FOREIGN KEY (day_id)\
    \ REFERENCES Days(None),\n    FOREIGN KEY (business_id) REFERENCES Business(None)\n\
    );"
  - "CREATE TABLEstate (\n    state_name TEXT PRIMARY KEY,\n    population INTEGER,\n\
    \    area double,\n    country_name varchar(3),\n    capital TEXT,\n    density\
    \ double\n);"
- source_sentence: '''Mad Max: Fury Road'' filminde çalışan 10 ekibin işlerinin yanı
    sıra listeleyin.'
  sentences:
  - "CREATE TABLEmovie (\n    movie_id INTEGER PRIMARY KEY,\n    title TEXT,\n   \
    \ budget INTEGER,\n    homepage TEXT,\n    overview TEXT,\n    popularity REAL,\n\
    \    release_date DATE,\n    revenue INTEGER,\n    runtime INTEGER,\n    movie_status\
    \ TEXT,\n    tagline TEXT,\n    vote_average REAL,\n    vote_count INTEGER\n);"
  - "CREATE TABLEstudent (\n    STU_NUM INT PRIMARY KEY,\n    STU_LNAME varchar(15),\n\
    \    STU_FNAME varchar(15),\n    STU_INIT varchar(1),\n    STU_DOB datetime,\n\
    \    STU_HRS INT,\n    STU_CLASS varchar(2),\n    STU_GPA float(8),\n    STU_TRANSFER\
    \ numeric,\n    DEPT_CODE varchar(18),\n    STU_PHONE varchar(4),\n    PROF_NUM\
    \ INT,\n    FOREIGN KEY (DEPT_CODE) REFERENCES DEPARTMENT(DEPT_CODE)\n);"
  - "CREATE TABLEFinancial_transactions (\n    transaction_id INTEGER,\n    account_id\
    \ INTEGER,\n    invoice_number INTEGER,\n    transaction_type VARCHAR(15),\n \
    \   transaction_date DATETIME,\n    transaction_amount DECIMAL(19,4),\n    transaction_comment\
    \ VARCHAR(255),\n    other_transaction_details VARCHAR(255),\n    FOREIGN KEY\
    \ (account_id) REFERENCES Accounts(account_id),\n    FOREIGN KEY (invoice_number)\
    \ REFERENCES Invoices(invoice_number)\n);"
- source_sentence: Tüm müşterilerin ortalama yaşının %80'inden daha büyük yaştaki
    müşterilerin gelirlerini ve sakin sayısını listeler misiniz?
  sentences:
  - "CREATE TABLECustomers (\n    ID INTEGER PRIMARY KEY,\n    SEX TEXT,\n    MARITAL_STATUS\
    \ TEXT,\n    GEOID INTEGER,\n    EDUCATIONNUM INTEGER,\n    OCCUPATION TEXT,\n\
    \    age INTEGER,\n    FOREIGN KEY (GEOID) REFERENCES Demog(None)\n);"
  - "CREATE TABLEauthors (\n    authID INTEGER PRIMARY KEY,\n    lname TEXT,\n   \
    \ fname TEXT\n);"
  - "CREATE TABLEcoaches (\n    coachID TEXT PRIMARY KEY,\n    year INTEGER,\n   \
    \ tmID TEXT,\n    lgID TEXT,\n    stint INTEGER,\n    won INTEGER,\n    lost INTEGER,\n\
    \    post_wins INTEGER,\n    post_losses INTEGER,\n    FOREIGN KEY (tmID) REFERENCES\
    \ teams(tmID),\n    FOREIGN KEY (year) REFERENCES teams(year)\n);"
- source_sentence: Eleanor Hunt'a ait kaç tane kiralama kimliği var?
  sentences:
  - "CREATE TABLEsinger (\n    Singer_ID INT PRIMARY KEY,\n    Name TEXT,\n    Country\
    \ TEXT,\n    Song_Name TEXT,\n    Song_release_year TEXT,\n    Age INT,\n    Is_male\
    \ bool\n);"
  - "CREATE TABLEdistrict (\n    District_ID INT PRIMARY KEY,\n    District_name TEXT,\n\
    \    Headquartered_City TEXT,\n    City_Population REAL,\n    City_Area REAL\n\
    );"
  - "CREATE TABLEcustomer (\n    customer_id INTEGER PRIMARY KEY,\n    store_id INTEGER,\n\
    \    first_name TEXT,\n    last_name TEXT,\n    email TEXT,\n    address_id INTEGER,\n\
    \    active INTEGER,\n    create_date DATETIME,\n    last_update DATETIME,\n \
    \   FOREIGN KEY (address_id) REFERENCES address(None),\n    FOREIGN KEY (store_id)\
    \ REFERENCES store(None)\n);"
---

# SentenceTransformer based on sentence-transformers/paraphrase-xlm-r-multilingual-v1

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-xlm-r-multilingual-v1](https://huggingface.co/sentence-transformers/paraphrase-xlm-r-multilingual-v1). 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:** [sentence-transformers/paraphrase-xlm-r-multilingual-v1](https://huggingface.co/sentence-transformers/paraphrase-xlm-r-multilingual-v1) <!-- at revision 9df88080e03c09181139a92fdf0423f84a79852d -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **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': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (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})
)
```

## 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("nypgd/fine-tuned-sentence-transformer_last")
# Run inference
sentences = [
    "Eleanor Hunt'a ait kaç tane kiralama kimliği var?",
    'CREATE TABLEcustomer (\n    customer_id INTEGER PRIMARY KEY,\n    store_id INTEGER,\n    first_name TEXT,\n    last_name TEXT,\n    email TEXT,\n    address_id INTEGER,\n    active INTEGER,\n    create_date DATETIME,\n    last_update DATETIME,\n    FOREIGN KEY (address_id) REFERENCES address(None),\n    FOREIGN KEY (store_id) REFERENCES store(None)\n);',
    'CREATE TABLEdistrict (\n    District_ID INT PRIMARY KEY,\n    District_name TEXT,\n    Headquartered_City TEXT,\n    City_Population REAL,\n    City_Area REAL\n);',
]
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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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 38,739 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                        | sentence_1                                                                         |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             |
  | details | <ul><li>min: 7 tokens</li><li>mean: 19.22 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 73.6 tokens</li><li>max: 128 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                             | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                     |
  |:-------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>en büyük alana sahip eyaleti belirtin</code>                                                     | <code>CREATE TABLEstate (<br>    state_name TEXT PRIMARY KEY,<br>    population INTEGER,<br>    area double,<br>    country_name varchar(3),<br>    capital TEXT,<br>    density double<br>);</code>                                                                                                                                                                                                           |
  | <code>Law & Order'ın hangi bölümleri Primetime Emmy Ödülleri'ne aday gösterildi?</code>                | <code>CREATE TABLEAward (<br>    award_id INTEGER PRIMARY KEY,<br>    organization TEXT,<br>    year INTEGER,<br>    award_category TEXT,<br>    award TEXT,<br>    series TEXT,<br>    episode_id TEXT,<br>    person_id TEXT,<br>    role TEXT,<br>    result TEXT,<br>    FOREIGN KEY (person_id) REFERENCES Person(person_id),<br>    FOREIGN KEY (episode_id) REFERENCES Episode(episode_id)<br>);</code> |
  | <code>Albümü "Universal Music Group" etiketi altında yer alan tüm şarkıların isimleri nelerdir?</code> | <code>CREATE TABLEtracklists (<br>    AlbumId INTEGER PRIMARY KEY,<br>    Position INTEGER,<br>    SongId INTEGER,<br>    FOREIGN KEY (AlbumId) REFERENCES Albums(AId),<br>    FOREIGN KEY (SongId) REFERENCES Songs(SongId)<br>);</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

- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `multi_dataset_batch_sampler`: round_robin

#### 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`: 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
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `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`: 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}
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch  | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.2064 | 500  | 0.5621        |
| 0.4129 | 1000 | 0.295         |
| 0.6193 | 1500 | 0.2644        |
| 0.8258 | 2000 | 0.2035        |
| 1.0322 | 2500 | 0.184         |
| 1.2386 | 3000 | 0.1237        |
| 1.4451 | 3500 | 0.1008        |
| 1.6515 | 4000 | 0.0984        |
| 1.8580 | 4500 | 0.0841        |
| 0.2064 | 500  | 0.1214        |
| 0.4129 | 1000 | 0.1139        |
| 0.6193 | 1500 | 0.11          |
| 0.8258 | 2000 | 0.0999        |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.4.0+cu121
- Accelerate: 0.32.1
- Datasets: 2.21.0
- Tokenizers: 0.19.1

## 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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