metadata
license: mit
datasets:
- ZeynepAltundal/w
language:
- tr
base_model:
- ytu-ce-cosmos/turkish-gpt2-medium-350m-instruct-v0.1
pipeline_tag: text-generation
library_name: transformers
tags:
- Turkish
- Fine-tuned
- Question-Answering
- GPT-2
Model Overview:
This model is a fine-tuned version of the "ytu-ce-cosmos/turkish-gpt2-medium-350m-instruct-v0.1", designed specifically for Turkish Question-Answering (Q&A). The fine-tuning process utilized a custom dataset generated from Turkish Wikipedia articles, focusing on factual knowledge.
Base Model: ytu-ce-cosmos/turkish-gpt2-medium-350m-instruct-v0.1 Fine-Tuned Dataset: Custom Turkish Q&A dataset Evaluation Loss: 2.1461 (on the validation dataset)
Quick Start
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "./fine_tuned_model" # Replace with your Hugging Face model path if uploaded
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
question = "Kamu sosyolojisi nedir?"
input_ids = tokenizer(question, return_tensors="pt").input_ids
output = model.generate(
input_ids=input_ids,
max_length=50,
num_return_sequences=1,
temperature=0.7
)
response = tokenizer.decode(output[0], skip_special_tokens=True)
print(f"Question: {question}")
print(f"Answer: {response}")
Training Details:
Dataset Source: Custom dataset generated from Turkish Wikipedia Number of Training Examples: 2,606 Training Dataset Size: 2,084 (80%) Validation Dataset Size: 522 (20%) Number of Epochs: 3 Batch Size: 8 Learning Rate: 5e-5 Evaluation Loss: 2.1461