Artificial intelligence has been taking over slowly and gradually. It has become even more evident for businesses and companies to make an investment in and use it for their business gain. The data-driven companies particularly can make the most of it. Developing an AI model for the English language can be somewhat of a smooth practice however, working on an AI model in Hindi language can be a bumpy ride for obvious reasons. There are languages that go well with the search engines and their machine tools however, Hindi is certainly none of these. Hindi language can pose great challenges due to its grammar composition, writing style, and font, etc. AI models can do extremely well in analyzing data, working on patterns, and making predictions. It is important to work with a local Hindi translation company to ensure your Hindi AI model understands and responds to nuances in Hindi language and its cultural aspects.
If you are a business that plans to work on an AI model in the Hindi language for your Indian masses and Hindi speakers across the globe, you must be mindful of the following factors to navigate through the Hindi language challenges.
Data Acquisition and Quality
First of all, it is important that you understand the process of AI model development and the integration of the Hindi language accordingly.
Text compilation
Building a Hindi language AI model may require high-quality text data. You may need a translation of this text as well in Hindi language for better understanding. The data may include news articles, books, social media chats, and texts as well as code repositories in Hindi language.
Data cleaning and preprocessing
The next process you need to consider and do your research is about data cleaning and processing with the help of translation. Your raw text data may contain inaccuracies and errors. Make sure that you hire a team of linguists who can take care of the content in Hindi language and review and edit the content making it error-free. Any minor negligence in the Hindi language, command, or translation can make the AI model malfunction. Therefore, cleaning and looking into pre-processing steps is extremely crucial to ensure that the data used is accurate and is good enough to train the AI model for the Hindi language.
Data diversity
The Hindi language is one of the most popular languages of recent times. It works with Devanagari writing script and has various dialects. First, you need to identify the dialects of the Hindi language that you want to instill in your Hindi-based AI model. As the Hindi language has multiple writing styles, sorting the preferred writing font and dialect in advance can make you excel in your work. You may consider adding data from different regions and genres to work on a model that can handle the diversity of the Hindi language including its dialects, nuances, and writing patterns. Moreover, collaborating with a professional translation services company with native speakers of the Hindi language is a must to evaluate the accuracy of results.
Model Selection and Training:
The next main factor is to select your model and train it accordingly.
Choosing the right architecture
Make sure that you choose the right architecture for your Hindi language AI model. There are many AI model architectures that can be considered suitable for the natural language processing tasks in the Hindi language. The popular options may include long short-term memory also known as an LSTM network, and transformers, these all can capture long-range dependencies in terms of text data more effectively.
Adapting existing models
You can also adapt the existing models for your Hindi language. The pre-trained models that have been in the English language containing datasets can be fine-tuned for the Hindi language by working on accurate translation with the help of a competent Hindi translation company and leveraging the transfer learning techniques. It is a good approach to start with, however do not overlook the factor of further training with high-quality Hindi data and an appropriate Hindi language translation. When you are using an existing model, you need to be extra vigilant in terms of translation from English to Hindi to avoid mistakes and malfunctioning of your AI model.
Taking help from existing Hindi AI models
You can also take help from the AI models that have already been excelling in the Hindi language by meeting the developers and companies, and looking into the execution process.
Dhenu 1.0 by Kissan AI
Dhenu is a large language model (LLM) that can actually support multiple languages. However, this particular AI model focuses greatly on the agricultural sector and contains and works with a massive dataset of Hindi text and code that is related to agriculture. It helps in making communication better and makes the information accessible for the farmers targeting rural India.
OpenHathi-Hi-v0.1 by Sarvam AI
OpenHathi which has been developed by Sarvam AI has emerged as a significant step toward a large and open-source language model in the Hindi language. It has been trained on the dataset of 10 billion tokens of Hindi text and also demonstrates the potential for open collaboration in the development of a powerful Hindi language model.
Conclusion
Developing an AI model in the Hindi language can be a testing process. However, working on it with the proper research can make the process somewhat convenient. Make sure that you work on text compilation, data cleaning, and preprocessing along with data diversity according to Hindi language requirements. Moreover, you should not overlook the need for accurate Hindi translation and always collaborate with a local Hindi translation vendor for accurate results. The selection of a model and its training is also an imperative part of the process.
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