---
library_name: transformers
language:
- ku
---

# Model Card for Model ID

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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-



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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
- **Funded by [optional]:** 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]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

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### Direct Use

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)

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[More Information Needed]

### 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

Use the code below to get started with the model.

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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](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **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]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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