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gpt2-turkish-10m

Text Generation
Transformers
PyTorch
Turkish
gpt2
text-generation-inference
Inference Endpoints
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metadata
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]

  • Repository: [More Information Needed]
  • Paper [optional]: [More Information Needed]
  • Demo [optional]: [More Information Needed]

Sınırlamalar ve Önyargılar

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 

# Example code for inference with the model

from transformers import GPT2Tokenizer, GPT2LMHeadModel

model_name = "cenker-sisman/gpt-turkish"  # 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

Preprocessing [optional]

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Training Hyperparameters

  • Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

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Evaluation

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

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]

BibTeX:

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APA:

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Model Card Authors [optional]

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Model Card Contact

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