shkna1368/v1-Kurdana

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Model Details

ئەم مۆدێلە لەسەر ٦١١٦ شێعر لە ٨٧ کتێب لە ٢١ شاعیرەوە فێر کراوە

این مدل با ٦١١٦ شعر از٨٧ کتاب از ۲۱شاعر تعلیم داده شده است

This model has been trained with 6116 poems from 87 books by 21 poets.

Model Description

Data for fine tune:

هەژار هێمن- پیرەمێرد- قانع- گۆران- وەفایی- نالی- جەلال مەلەکشا- شێرکۆ بێکەس- مەحوی- هێدی- جگەرخوێن- دڵشاد مەریوانی- سابیری- کەمالی- کامەران موکری- ئەخۆل- حەقیقی- سوارە ئیلخانیزادە- نافیع مەزهەر-

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
• 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

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-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)

Training Details

Training Data

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Training Procedure

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Training Hyperparameters

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Evaluation

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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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