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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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  ---
 
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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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.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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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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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
 
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
 
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
 
 
 
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
 
 
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
 
 
 
 
 
 
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
 
 
 
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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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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- ### Results
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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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- #### Hardware
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- #### Software
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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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- **APA:**
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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 [optional]
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- ## Model Card Authors [optional]
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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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  ---
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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.