Add new SentenceTransformer model.

b91fddb verified 6 months ago

18.1 kB

	---
	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]
	```

	<!--
	### Direct Usage (Transformers)

	<details><summary>Click to see the direct usage in Transformers</summary>

	</details>
	-->

	<!--
	### 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

	#### 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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