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--- |
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widget: |
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- text: "[Q] cengiz han binbasi olarak kim atadi" |
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example_title: "Örnek 1" |
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- text: "[Q] 2003 dunya halter sampiyonasi hangi tarihlerde yapildi" |
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example_title: "Örnek 2" |
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- text: "[Q] ahmet haldun dormen hangi tarihte dogmustur" |
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example_title: "Örnek 3" |
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- text: "[Q] ender dogan kimdir" |
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example_title: "Örnek 4" |
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- text: "[Q] isil kasapoglu nun meslegi nedir" |
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example_title: "Örnek 5" |
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- text: "[Q] mustafa sagyasar ankara radyosu nda ne yapti" |
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example_title: "Örnek 6" |
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- text: "[Q] behiye aksoy kac yasinda vefat etmistir" |
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example_title: "Örnek 7" |
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- text: "[Q] tekken in kelime ismi nedir" |
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example_title: "Örnek 8" |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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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. |
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## Önemli Notlar |
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* Inference için soruların başında [Q] kullanılmalıdır. |
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* Sorular latin karakterlerden oluşmalıdır. ç ğ I ö ş ü gibi harfler içermemelidir. |
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* Sorular küçük harflerden oluşmalıdır. Büyük harf veya sembol kullanımı farklı ve istenmeyen cevaplar üretecektir. |
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## Model Details |
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## Başlıklara göre en fazla soru cevap içeren konular aşağıdadır: |
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* Futbol rekabetleri listesi: 313 adet |
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* Cengiz Han: 310 adet |
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* Triple H: 196 adet |
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* Lüleburgaz Muharebesi: 158 adet |
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* Zümrüdüanka Yoldaşlığı: 155 adet |
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* Shakespeare eserleri çevirileri listesi: 145 adet |
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* Kırkpınar Yağlı Güreşleri: 142 adet |
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* Sovyetler Birliği'nin askerî tarihi: 136 adet |
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* I. Baybars: 135 adet |
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* Dumbledore'un Ordusu: 126 adet |
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* Nicolaus Copernicus: 119 adet |
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* Ermenistan Sovyet Sosyalist Cumhuriyeti: 111 adet |
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* Boshin Savaşı: 99 adet |
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* Suvorov Harekâtı: 98 adet |
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* Gökhan Türkmen: 96 adet |
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* Wolfgang Amadeus Mozart: 95 adet |
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* Joachim von Ribbentrop: 95 adet |
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* Rumyantsev Harekâtı: 94 adet |
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* Hermann Göring: 93 adet |
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* Nâzım Hikmet: 90 adet |
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* Said Nursî: 90 adet |
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* Emîn: 88 adet |
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* Antonio Gramsci: 87 adet |
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* Gilles Deleuze: 86 adet |
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* Madagaskar: 86 adet |
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* Faşizm: 85 adet |
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* Mac OS X Snow Leopard: 85 adet |
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* Korsun-Şevçenkovski Taarruzu: 84 adet |
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* Soğuk Savaş: 84 adet |
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* Adolf Eichmann: 83 adet |
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* Niccolò Paganini: 83 adet |
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* II. Dünya Savaşı tankları: 81 adet |
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* Pergamon: 81 adet |
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* IV. Mihail: 80 adet |
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* Bolşeviklere karşı sol ayaklanmalar: 77 adet |
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* Osman Gazi: 77 adet |
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* V. Leon: 76 adet |
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* Ajda Pekkan: 75 adet |
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* Mehdi Savaşı: 75 adet |
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* Tsushima Muharebesi: 73 adet |
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* Mehdî (Abbâsî halifesi): 72 adet |
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* Franck Ribéry: 72 adet |
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* I. Basileios: 69 adet |
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* Antimon: 68 adet |
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* Kolomb öncesi Amerika: 68 adet |
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* Otto Skorzeny: 68 adet |
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* Kâzım Koyuncu: 68 adet |
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* İmamiye (Şiilik öğretisi): 66 adet |
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* Oscar Niemeyer: 66 adet |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** Cenker Sisman |
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- **Model type:** [More Information Needed] |
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- **Language(s) (NLP):** [More Information Needed] |
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- **License:** [More Information Needed] |
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- **Finetuned from model :** redrussianarmy/gpt2-turkish-cased |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [More Information Needed] |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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[More Information Needed] |
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### Downstream Use [optional] |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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[More Information Needed] |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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[More Information Needed] |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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[More Information Needed] |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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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. |
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## How to Get Started with the Model |
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```python |
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"""Inference""" |
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from transformers import PreTrainedTokenizerFast, GPT2LMHeadModel, GPT2TokenizerFast, GPT2Tokenizer |
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def load_model(model_path): |
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model = GPT2LMHeadModel.from_pretrained(model_path) |
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return model |
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def load_tokenizer(tokenizer_path): |
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tokenizer = GPT2Tokenizer.from_pretrained(tokenizer_path) |
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return tokenizer |
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def generate_text(model_path, sequence, max_length): |
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model = load_model(model_path) |
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tokenizer = load_tokenizer(model_path) |
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ids = tokenizer.encode(sequence, return_tensors='pt') |
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outputs = model.generate( |
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ids, |
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do_sample=True, |
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max_length=max_length, |
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pad_token_id=model.config.eos_token_id, |
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top_k=1, |
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top_p=0.99, |
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) |
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converted = tokenizer.convert_ids_to_tokens(outputs[0]) |
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valid_tokens = [token if token is not None else '.' for token in converted] |
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generated_text = tokenizer.convert_tokens_to_string(valid_tokens) |
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print(generated_text) |
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model2_path = "cenkersisman/chatbotgpt-turkish" |
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sequence2 = "[Q] cengiz han kimdir" |
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max_len = 120 |
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generate_text(model2_path, sequence2, max_len) |
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``` |
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## Training Details |
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### Training Data |
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<!-- 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. --> |
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[More Information Needed] |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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#### Preprocessing [optional] |
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[More Information Needed] |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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[More Information Needed] |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Data Card if possible. --> |
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[More Information Needed] |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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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). |
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- **Hardware Type:** [More Information Needed] |
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- **Hours used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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[More Information Needed] |
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#### Software |
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[More Information Needed] |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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[More Information Needed] |
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## More Information [optional] |
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[More Information Needed] |
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## Model Card Authors [optional] |
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[More Information Needed] |
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## Model Card Contact |
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[More Information Needed] |
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