🚀 Model Card for Model ID
This is the model card of a 🤗 transformers model pushed on the Hub. It provides details about a model trained with Kurdish poetry data, including its development, usage, training, and evaluation aspects.
🚀 Quick Start
To get started with this model, you can use the following code:
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("shkna1368/v1-Kurdana")
model = AutoModelForSeq2SeqLM.from_pretrained("shkna1368/v1-Kurdana")
input_ids = tokenizer.encode(question, return_tensors="pt")
output_ids = model.generate(input_ids, max_length=1200, num_beams=200, early_stopping=False)
answer = tokenizer.decode(output_ids[0], skip_special_tokens=True)
✨ Features
- Trained on Kurdish Poetry: This model has been trained with 6116 poems from 87 books by 21 poets, enabling it to handle tasks related to Kurdish poetry.
- Fine - tuned from mt5: It is fine - tuned from the mt5 model, potentially leveraging the pre - trained knowledge of mt5.
📦 Installation
No specific installation steps are provided in the original document.
💻 Usage Examples
Basic Usage
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("shkna1368/v1-Kurdana")
model = AutoModelForSeq2SeqLM.from_pretrained("shkna1368/v1-Kurdana")
input_ids = tokenizer.encode(question, return_tensors="pt")
output_ids = model.generate(input_ids, max_length=1200, num_beams=200, early_stopping=False)
answer = tokenizer.decode(output_ids[0], skip_special_tokens=True)
📚 Documentation
Model Details
This model has been trained with 6116 poems from 87 books by 21 poets.
Model Description
- Data for fine - tune:
- Hezar
- Hemin
- Piramerd
- Qane
- Goran
- Wefayi
- Nali
- Jalal Melaksha
- Sherko Bekes
- Mehwi
- Hedi
- Jigerxwen
- Delshad Merivani
- Sabiri
- Kamali
- Kameran Mokri
- Akhol
- Haqigi
- Sware Ilkhanzade
- Nafie Mezher
![Uploading image.png…]()
- Developed by: Shabab Koohi
- Funded by [optional]: Shabab Koohi
- Connect to developer: https://www.linkedin.com/in/shabab-koohi/
- Shared by [optional]: [More Information Needed]
- Model type: [More Information Needed]
- Language(s) (NLP): [More Information Needed]
- License: [More Information Needed]
- Finetuned from model [optional]: mt5
Model Sources [optional]
- Repository: https://github.com/shkna1368/kurdana/
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
No specific details are provided in the original document.
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.
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
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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
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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