v1-Hawramiana

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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

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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]

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• Repository: https://github.com/shkna1368/kurdana/
• Paper [optional]: [More Information Needed]
• Demo [optional]: https://huggingface.co/spaces/shkna1368/Hawramiana

Uses

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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

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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.

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

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Preprocessing [optional]

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Training Hyperparameters

• Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

Speeds, Sizes, Times [optional]

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Evaluation

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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

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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• Hours used: [More Information Needed]
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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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