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yasarefe/tai-1.1
tai-1.1 is a text classification model from yasarefe. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
TAI-1.1 (Turkish AI 1.1) is a fine-tuned BERT model for Turkish language understanding and processing tasks. This model is based on the "dbmdz/bert-base-turkish-cased" architecture and has been further trained on a di…
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From the Hugging Face model README
TAI-1.1 (Turkish AI 1.1) is a fine-tuned BERT model for Turkish language understanding and processing tasks. This model is based on the "dbmdz/bert-base-turkish-cased" architecture and has been further trained on a diverse Turkish text dataset.
You can use this model directly with the Hugging Face Transformers library:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("yasarefe/tai-1.1")
model = AutoModelForSequenceClassification.from_pretrained("yasarefe/tai-1.1")
# Example usage
text = "Bu bir örnek Türkçe cümledir."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
This model can be fine-tuned for various downstream tasks such as sentiment analysis, text classification, or named entity recognition. Here's an example of how to fine-tune the model:
from transformers import Trainer, TrainingArguments
training_args = TrainingArguments(
output_dir="./results",
num_train_epochs=3,
per_device_train_batch_size=16,
per_device_eval_batch_size=64,
warmup_steps=500,
weight_decay=0.01,
logging_dir="./logs",
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
)
trainer.train()
While TAI-1.1 has been trained on a diverse dataset, it may still have biases or limitations:
If you use this model in your research, please cite:
@misc{tai-1.1,
author = {Yasar Efe},
title = {TAI-1.1: Turkish Language Model},
year = {2024},
publisher = {HuggingFace},
journal = {HuggingFace Model Hub},
howpublished = {\url{https://huggingface.co/yasarefe/tai-1.1}},
}
For any questions or feedback, please open an issue on the model repository or contact the author directly.