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How to use ChatGPT in my company

During the last few weeks you have surely seen how ChatGTP is capable of answering any question that we ask, it is even capable of generating texts. But is it useful for our business? Can you use it as an assistant for your clients or users?

The answer is “Yes, but…”.

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Cómo usar ChatGPT en mi empresa

Durante las últimas semanas seguro que has visto cómo ChatGTP es capaz de contestar a cualquier pregunta que le hagamos, incluso es capaz de generar textos. Pero, ¿es útil para nuestro negocio? ¿Puedes utilizarlo como asistente para tus clientes o usuarios?

La respuesta es “Sí, pero…”.

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Integration with Hugging Face Model Hub and more

We are now integrated with Hugging Face Model Hub and it is very easy to use a model hosted there for model-assisted labeling...

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The importance of annotating the right dataset

High-quality data is the foundation of any successful machine learning project. It's also a time consuming task, so it pays off to be sure we have the right dataset before starting the data annotation process...

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Why should we train a pre-trained Machine Learning model

Natural Language Processing, or NLP, is long overdue to stay. Thanks to the predictive models of Machine Learning, or ML, on which the NLP is based, we can automate tasks such as classifying incidents, translating texts, detecting the sentiment in a phone call, answering questions or even writing software...

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Why is Data Annotation so important?

AI: it’s the Data, stupid. —  We have moved beyond the time where talking about Artificial Intelligence (AI) was almost science fiction. AI is nowadays pretty much everywhere and real everyday use cases can be found in our phone apps, automobiles, finance products, marketing campaigns, advances in healthcare and in most business decisions. The hype for…

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Por qué debemos entrenar un modelo de Machine Learning preentrenado

El procesamiento de lenguaje natural, o NLP, según sus siglas en inglés, llegó hace tiempo para quedarse. Gracias a los modelos predictivos de aprendizaje automático, o ML según sus siglas en inglés, sobre los que se basa el NLP podemos automatizar tareas como clasificar incidencias, traducir textos, detectar el sentimiento en una llamada telefónica, responder preguntas o incluso escribir código de programación...

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