Hugging Face's logo

shkna1368
/
v1-Hawramiana

Transformers
Safetensors
Kurdish
mt5
text2text-generation
Model card Files
xet
 Community

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]

Uses

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

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)

[More Information Needed]

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

  • Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

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

  • 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]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]

Downloads last month
6
Safetensors
Model size
0.6B params
Tensor type
F32
·
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Space using shkna1368/v1-Hawramiana 1

Paper for shkna1368/v1-Hawramiana