README.md

---
widget:
- text: 'cengiz'
  example_title: Örnek 1
language:
- tr
---

# Model Card for Model ID

Model Card for GPT-2 Turkish Model

  
## Model Details

### Model Description

GPT-2 Turkish Model is a language model based on the GPT-2 architecture, fine-tuned for the Turkish language. It is capable of generating human-like text based on a given prompt and has been trained on a large corpus of Turkish text data.



- **Developed by:** Cenker Sisman
- **Model type:** 
- **Language(s) (NLP):** 
- **License:** 
- **Finetuned from model  :** GPT-2
    
### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]



## Sınırlamalar ve Önyargılar

## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model
```python
# Example code for inference with the model

from transformers import GPT2Tokenizer, GPT2LMHeadModel

model_name = "cenker-sisman/gpt2-turkish-small"  # Change to the model name you have uploaded to Hugging Face
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)

prompt = "cengiz"
input_ids = tokenizer.encode(prompt, return_tensors="pt")
output = model.generate(input_ids, max_length=100, pad_token_id=tokenizer.eos_token_id)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

```
## Training Details

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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