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library_name: transformers
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license: cc-by-nc-4.0
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---
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##
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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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.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- 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 Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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---
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library_name: transformers
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license: cc-by-nc-4.0
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datasets:
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- tahrirchi/dilmash
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tags:
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- nllb
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- karakalpak
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language:
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- en
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- ru
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- uz
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- kaa
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base_model: facebook/nllb-200-distilled-600M
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pipeline_tag: translation
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# Dilmash: Karakalpak Machine Translation Models
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This repository contains a collection of machine translation models for the Karakalpak language, developed as part of the research paper "Open Language Data Initiative: Advancing Low-Resource Machine Translation for Karakalpak".
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## Model variations
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We provide three variants of our Karakalpak translation model:
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| Model | Base Model | Parameters | Tokenizer Length | Datasets | Languages |
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|-------|------------|------------|-------------------|----------|-----------|
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| [`dilmash-raw`](https://huggingface.co/tahrirchi/dilmash-raw) | [nllb-200-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) | 615M | 256,204 | [Dilmash corpus](https://huggingface.co/datasets/tahrirchi/dilmash) | Karakalpak, Uzbek, Russian, English |
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| **[`dilmash`](https://huggingface.co/tahrirchi/dilmash)** | **[nllb-200-600M](https://huggingface.co/facebook/nllb-200-distilled-600M)** | **629M** | **269,399** | **[Dilmash corpus](https://huggingface.co/datasets/tahrirchi/dilmash)** | **Karakalpak, Uzbek, Russian, English** |
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| [`dilmash-TIL`](https://huggingface.co/tahrirchi/dilmash-TIL) | [nllb-200-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) | 629M | 269,399 | [Dilmash corpus](https://huggingface.co/datasets/tahrirchi/dilmash), TIL corpus | Karakalpak, Uzbek, Russian, English |
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## Intended uses & limitations
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These models are designed for machine translation tasks involving the Karakalpak language. They can be used for translation between Karakalpak, Uzbek, Russian, or English.
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### How to use
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You can use these models with the Transformers library. Here's a quick example:
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```python
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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model_ckpt = "tahrirchi/dilmash"
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tokenizer = AutoTokenizer.from_pretrained(model_ckpt)
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model = AutoModelForSeq2SeqLM.from_pretrained(model_ckpt)
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# Example translation
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input_text = "Here is dilmash translation model."
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tokenizer.src_lang = "eng_Latn"
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tokenizer.tgt_lang = "kaa_Latn"
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inputs = tokenizer(input_text, return_tensors="pt")
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outputs = model.generate(**inputs)
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translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(translated_text) # Dilmash awdarması modeli.
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```
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## Training data
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The models were trained on a parallel corpus of 300,000 sentence pairs, including:
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- Uzbek-Karakalpak (100,000 pairs)
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- Russian-Karakalpak (100,000 pairs)
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- English-Karakalpak (100,000 pairs)
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The dataset is available [here](https://huggingface.co/datasets/tahrirchi/dilmash).
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## Training procedure
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For full details of the training procedure, please refer to our paper (coming soon!).
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## Citation
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If you use these models in your research, please cite our paper:
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```bibtex
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@inproceedings{mamasaidov2024advancing,
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title={Open Language Data Initiative: Advancing Low-Resource Machine Translation for Karakalpak},
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author={Mamasaidov, Mukhammadsaid and Shopulatov, Abror},
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booktitle={Proceedings of the OLDI Workshop},
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year={2024}
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}
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```
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## Gratitude
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We are thankful to these awesome organizations and people for helping to make it happen:
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- [David Dalé](https://daviddale.ru): for advise throughout the process
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- Perizad Najimova: for expertise and assistance with the Karakalpak language
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- [Nurlan Pirjanov](https://www.linkedin.com/in/nurlan-pirjanov/): for expertise and assistance with the Karakalpak language
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- [Atabek Murtazaev](https://www.linkedin.com/in/atabek/): for advise throughout the process
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- Ajiniyaz Nurniyazov: for advise throughout the process
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## Contacts
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We believe that this work will enable and inspire all enthusiasts around the world to open the hidden beauty of low-resource languages, in particular Karakalpak.
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For further development and issues about the dataset, please use [email protected] or [email protected] to contact.
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