🦖 Turkish LM Tuner

Overview¶

Turkish LM Tuner is a library for fine-tuning Turkish language models on various NLP tasks. It is built on top of Hugging Face Transformers library. It supports finetuning with conditional generation and sequence classification tasks. The library is designed to be modular and extensible. It is easy to add new tasks and models. The library also provides data loaders for various Turkish NLP datasets.

Installation¶

You can install turkish-lm-tuner via PyPI:

pip install turkish-lm-tuner

Alternatively, you can use the following command to install the library:

pip install git+https://github.com/boun-tabi-LMG/turkish-lm-tuner.git

Model Support¶

Any Encoder or ConditionalGeneration model that is compatible with Hugging Face Transformers library can be used with Turkish LM Tuner. The following models are tested and supported.

Task and Dataset Support¶

Task	Datasets
Text Classification	Product Reviews, TTC4900, Tweet Sentiment
Natural Language Inference	NLI_TR, SNLI_TR, MultiNLI_TR
Semantic Textual Similarity	STSb_TR
Named Entity Recognition	WikiANN, Milliyet NER
Part-of-Speech Tagging	BOUN, IMST
Text Summarization	TR News, MLSUM, Combined TR News and MLSUM
Title Generation	TR News, MLSUM, Combined TR News and MLSUM
Paraphrase Generation	OpenSubtitles, Tatoeba, TED Talks

Usage¶

The tutorials in the documentation can help you get started with turkish-lm-tuner.

Examples¶

Fine-tune and evaluate a conditional generation model¶

from turkish_lm_tuner import DatasetProcessor, TrainerForConditionalGeneration

dataset_name = "tr_news"
task = "summarization"
task_format="conditional_generation"
model_name = "boun-tabi-LMG/TURNA"
max_input_length = 764
max_target_length = 128
dataset_processor = DatasetProcessor(
    dataset_name=dataset_name, task=task, task_format=task_format, task_mode='',
    tokenizer_name=model_name, max_input_length=max_input_length, max_target_length=max_target_length
)

train_dataset = dataset_processor.load_and_preprocess_data(split='train')
eval_dataset = dataset_processor.load_and_preprocess_data(split='validation')
test_dataset = dataset_processor.load_and_preprocess_data(split="test")

training_params = {
    'num_train_epochs': 10,
    'per_device_train_batch_size': 4,
    'per_device_eval_batch_size': 4,
    'output_dir': './', 
    'evaluation_strategy': 'epoch',
    'save_strategy': 'epoch',
    'predict_with_generate': True    
}
optimizer_params = {
    'optimizer_type': 'adafactor',
    'scheduler': False,
}

model_trainer = TrainerForConditionalGeneration(
    model_name=model_name, task=task,
    optimizer_params=optimizer_params,
    training_params=training_params,
    model_save_path="turna_summarization_tr_news",
    max_input_length=max_input_length,
    max_target_length=max_target_length, 
    postprocess_fn=dataset_processor.dataset.postprocess_data
)

trainer, model = model_trainer.train_and_evaluate(train_dataset, eval_dataset, test_dataset)

model.save_pretrained(model_save_path)
dataset_processor.tokenizer.save_pretrained(model_save_path)

Evaluate a conditional generation model with custom generation config¶

from turkish_lm_tuner import DatasetProcessor, EvaluatorForConditionalGeneration

dataset_name = "tr_news"
task = "summarization"
task_format="conditional_generation"
model_name = "boun-tabi-LMG/TURNA"
task_mode = ''
max_input_length = 764
max_target_length = 128
dataset_processor = DatasetProcessor(
    dataset_name, task, task_format, task_mode,
    model_name, max_input_length, max_target_length
)

test_dataset = dataset_processor.load_and_preprocess_data(split="test")

test_params = {
    'per_device_eval_batch_size': 4,
    'output_dir': './',
    'predict_with_generate': True
}

model_path = "turna_tr_news_summarization"
generation_params = {
    'num_beams': 4,
    'length_penalty': 2.0,
    'no_repeat_ngram_size': 3,
    'early_stopping': True,
    'max_length': 128,
    'min_length': 30,
}
evaluator = EvaluatorForConditionalGeneration(
    model_path, model_name, task, max_input_length, max_target_length, test_params,
    generation_params, dataset_processor.dataset.postprocess_data
)
results = evaluator.evaluate_model(test_dataset)
print(results)

Reference¶

If you use this repository, please cite the following related paper:

@inproceedings{uludogan-etal-2024-turna,
    title = "{TURNA}: A {T}urkish Encoder-Decoder Language Model for Enhanced Understanding and Generation",
    author = {Uludo{\u{g}}an, G{\"o}k{\c{c}}e  and
      Balal, Zeynep  and
      Akkurt, Furkan  and
      Turker, Meliksah  and
      Gungor, Onur  and
      {\"U}sk{\"u}darl{\i}, Susan},
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-acl.600",
    doi = "10.18653/v1/2024.findings-acl.600",
    pages = "10103--10117",
}

License¶

Note that all datasets belong to their respective owners. If you use the datasets provided by this library, please cite the original source.

This code base is licensed under the MIT license. See LICENSE for details.