---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
widget:
- text: "[Q] cengiz han binbasi olarak kim atadi"
  example_title: "Örnek 1"
- text: "[Q] 2003 dunya halter sampiyonasi hangi tarihlerde yapildi"
  example_title: "Örnek 2"
- text: "[Q] ahmet haldun dormen hangi tarihte dogmustur"
  example_title: "Örnek 3"
- text: "[Q] ender dogan kimdir"
  example_title: "Örnek 4"
- text: "[Q] isil kasapoglu nun meslegi nedir"
  example_title: "Örnek 5"
- text: "[Q] mustafa sagyasar ankara radyosu nda ne yapti"
  example_title: "Örnek 6"
- text: "[Q] behiye aksoy kac yasinda vefat etmistir"
  example_title: "Örnek 7"
- text: "[Q] tekken in kelime ismi nedir"
  example_title: "Örnek 8"
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

Bu model test amaçlı hazırlanmıştır ve fikir vermesi açısından geliştirilmiştir. Model için Vikipedi üzerinden üretilen 40 bin soru cevap GPT ile eğitilmiştir. Daha büyük veri setlerinde daha iyi sonuçlar alınabilir.
## Model Details
## Başlıklara göre en fazla soru cevap içeren konular aşağıdadır:
* Futbol rekabetleri listesi: 313 adet
* Cengiz Han: 310 adet
* Triple H: 196 adet
* Lüleburgaz Muharebesi: 158 adet
* Zümrüdüanka Yoldaşlığı: 155 adet
* Shakespeare eserleri çevirileri listesi: 145 adet
* Kırkpınar Yağlı Güreşleri: 142 adet
* Sovyetler Birliği'nin askerî tarihi: 136 adet
* I. Baybars: 135 adet
* Dumbledore'un Ordusu: 126 adet
* Nicolaus Copernicus: 119 adet
* Ermenistan Sovyet Sosyalist Cumhuriyeti: 111 adet
* Boshin Savaşı: 99 adet
* Suvorov Harekâtı: 98 adet
* Gökhan Türkmen: 96 adet
* Wolfgang Amadeus Mozart: 95 adet
* Joachim von Ribbentrop: 95 adet
* Rumyantsev Harekâtı: 94 adet
* Hermann Göring: 93 adet
* Nâzım Hikmet: 90 adet
* Said Nursî: 90 adet
* Emîn: 88 adet
* Antonio Gramsci: 87 adet
* Gilles Deleuze: 86 adet
* Madagaskar: 86 adet
* Faşizm: 85 adet
* Mac OS X Snow Leopard: 85 adet
* Korsun-Şevçenkovski Taarruzu: 84 adet
* Soğuk Savaş: 84 adet
* Adolf Eichmann: 83 adet
* Niccolò Paganini: 83 adet
* II. Dünya Savaşı tankları: 81 adet
* Pergamon: 81 adet
* IV. Mihail: 80 adet
* Bolşeviklere karşı sol ayaklanmalar: 77 adet
* Osman Gazi: 77 adet
* V. Leon: 76 adet
* Ajda Pekkan: 75 adet
* Mehdi Savaşı: 75 adet
* Tsushima Muharebesi: 73 adet
* Mehdî (Abbâsî halifesi): 72 adet
* Franck Ribéry: 72 adet
* I. Basileios: 69 adet
* Antimon: 68 adet
* Kolomb öncesi Amerika: 68 adet
* Otto Skorzeny: 68 adet
* Kâzım Koyuncu: 68 adet
* İmamiye (Şiilik öğretisi): 66 adet
* Oscar Niemeyer: 66 adet

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** Cenker Sisman
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model  :** redrussianarmy/gpt2-turkish-cased
    ![Loss değerleri](https://huggingface.co/cenkersisman/chatbotgpt-turkish/resolve/main/lossdegerleri.jpg)
### Model Sources [optional]

<!-- Provide the basic links for the model. -->

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

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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
"""Inference"""

from transformers import PreTrainedTokenizerFast, GPT2LMHeadModel, GPT2TokenizerFast, GPT2Tokenizer

def load_model(model_path):
    model = GPT2LMHeadModel.from_pretrained(model_path)
    return model


def load_tokenizer(tokenizer_path):
    tokenizer = GPT2Tokenizer.from_pretrained(tokenizer_path)
    return tokenizer

def generate_text(model_path, sequence, max_length):
    model = load_model(model_path)
    tokenizer = load_tokenizer(model_path)
    ids = tokenizer.encode(sequence, return_tensors='pt')
    outputs = model.generate(
        ids,
        do_sample=True,
        max_length=max_length,
        pad_token_id=model.config.eos_token_id,
        top_k=1,
        top_p=0.99,
    )
    converted = tokenizer.convert_ids_to_tokens(outputs[0])
    valid_tokens = [token if token is not None else '.' for token in converted]
    generated_text = tokenizer.convert_tokens_to_string(valid_tokens)
    print(generated_text)

model2_path = "cenkersisman/chatbotgpt-turkish"
sequence2 = "[Q] cengiz han kimdir"
max_len = 120

generate_text(model2_path, sequence2, max_len)
```
## Training Details

### Training Data

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### 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]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Data Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## 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

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## 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:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

[More Information Needed]