Model Card for Fine-Tuned Transformers Model

Model Details

Model Description

This model was fine-tuned as part of an artificial intelligence course at Gazi University in Ankara using a custom dataset created by the students and instructors. The model is optimized for a specific task, such as sentiment analysis or text classification, in the Turkish language.

Developed by: Gazi University AI Course Team
Funded by [optional]: Gazi University
Shared by [optional]: Faculty Members and Students
Model type: Transformers-based language model (e.g., BERT or GPT)
Language(s) (NLP): Turkish
License: [CC BY-SA 4.0 or other appropriate license]
Finetuned from model [optional]: bert-base-turkish-cased (example)

Model Sources [optional]

Dataset that we used for fine-tuning: [https://www.kaggle.com/datasets/nezahatkk/grammar-correction-dataset-for-fine-tuning]

Uses

Direct Use

The model can be directly used for tasks such as text classification, sentiment analysis, or other natural language processing tasks in Turkish.

Downstream Use [optional]

The model can be integrated into larger ecosystems or more complex projects.

Out-of-Scope Use

The model should not be used for unethical or malicious purposes. Additionally, it may have limited performance for multilingual tasks.

Bias, Risks, and Limitations

This model may inherit biases present in the training dataset. It is designed for English, and performance may degrade for other languages or domains outside its training data.

Recommendations

Users are advised to be aware of the model's limitations due to its training dataset and validate its results for their specific use case.

How to Get Started with the Model

You can use the following code snippet to load and test the model:

from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load the model
model_name = "gazi-university/fine-tuned-turkish-model"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Example input
text = "This AI model works perfectly!"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)