library_name: transformers
language:
- ku
Model Card for Model ID
Model Details
ئەم مۆدێلە لەسەر ١٩٤ شێعر لە ١٨ کتێب لە ١٢ شاعیرەوە فێر کراوە
این مدل با ۱۹۴شعر از ۱۸کتاب از ۱۲شاعر تعلیم داده شده است
This model has been trained with 194 poems from 18 books by 12 poets
Data for fine tune:
مەولەوی- خانای قوبادی- وەلی دێوانە- سەیدی- بێسارانی- حیلمی کاکەیی- مەلا حەسەنی دزڵی- ئاغا عینایەت- فەقێ قادری هەمەوەند- جەهانئارا- مەلا مستەفای عاسی- میرزا عەبدولقادری پاوەیی- مەستوورە
Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: Shabab Koohi
- Connect to developer: https://www.linkedin.com/in/shabab-koohi/
- Funded by [optional]: Shabab koohi
- Shared by [optional]: Shabab Koohi
- 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]: https://huggingface.co/spaces/shkna1368/Hawramiana
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
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("shkna1368/v1-Hawramiana")
model = AutoModelForSeq2SeqLM.from_pretrained("shkna1368/v1-Hawramiana")
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)
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Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
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Speeds, Sizes, Times [optional]
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Evaluation
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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).
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Technical Specifications [optional]
Model Architecture and Objective
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