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

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## 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

<!-- Provide a longer summary of what this model is. -->

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]

<!-- Provide the basic links for the model. -->

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

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

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[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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)

[More Information Needed]

## Training Details

### Training Data

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[More Information Needed]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### 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]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

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#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

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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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