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
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
Model Description
- 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
Model Sources [optional]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
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
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
"""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
[More Information Needed]
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
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
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- 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]
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APA:
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Glossary [optional]
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Model Card Authors [optional]
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