---
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)
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
### 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]
```
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 38,739 training samples
* Columns: sentence_0 and sentence_1
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 7 tokens
- mean: 19.22 tokens
- max: 63 tokens
| - min: 11 tokens
- mean: 73.6 tokens
- max: 128 tokens
|
* Samples:
| sentence_0 | sentence_1 |
|:-------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| en büyük alana sahip eyaleti belirtin | CREATE TABLEstate (
state_name TEXT PRIMARY KEY,
population INTEGER,
area double,
country_name varchar(3),
capital TEXT,
density double
); |
| Law & Order'ın hangi bölümleri Primetime Emmy Ödülleri'ne aday gösterildi? | CREATE TABLEAward (
award_id INTEGER PRIMARY KEY,
organization TEXT,
year INTEGER,
award_category TEXT,
award TEXT,
series TEXT,
episode_id TEXT,
person_id TEXT,
role TEXT,
result TEXT,
FOREIGN KEY (person_id) REFERENCES Person(person_id),
FOREIGN KEY (episode_id) REFERENCES Episode(episode_id)
); |
| Albümü "Universal Music Group" etiketi altında yer alan tüm şarkıların isimleri nelerdir? | CREATE TABLEtracklists (
AlbumId INTEGER PRIMARY KEY,
Position INTEGER,
SongId INTEGER,
FOREIGN KEY (AlbumId) REFERENCES Albums(AId),
FOREIGN KEY (SongId) REFERENCES Songs(SongId)
); |
* Loss: [MultipleNegativesRankingLoss](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
Click to expand
- `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