Our AI experts, proficient and experienced in ML and NLP technologies, specialize in building domain-specific Large Language Models (LLMs) to cater to your company’s proprietary data.
Determines the most logical next word or phrase to add to a sequence using historical text data and previous text inputs.
Process and generate text effectively, capturing long-range dependencies and contextual information.
Create a fixed-length representation out of the input data that the decoder will use to create the output sequence.
Receiving text in various languages, processing it, and producing text in various languages.
Adopting large language models with pre-training to comprehend language and semantics broadly. Using smaller task-specific datasets, these pre-trained models can then be refined for use in particular tasks or domains.
Incorporated into LLMs to record sequential dependencies and combine various architectures for better performance.
Gathering information for model quality assessment and training (if fine-tuning).
Dividing the input and output texts into manageable chunks that the LLM AI models can process.
Process of training a model on a large corpus of text, helping the model to learn the structure of the language and grammar.
Captures contextual relationships, which enables LLMs to comprehend and produce text.
With the help of a sizable new dataset, an LLM segment is retrained while being more closely tailored to the requirements of a given task.
Iteratively predicts the following token in a sequence to produce text outputs in response to an input prompt.
Accurately assess the nuances of a question and offer responses that are appropriate for the situation.
Find the most likely sequence of words given a set of input tokens.
Rephrase your canned responses while considering the conversation's context.
Understanding the context, entities, relationships, and sentiments from textual data through analysis and meaning extraction.
Transforming spoken language into text so that it can be processed by computers.
Generating text or speech that sounds human-like by using input or specific patterns; is frequently used in chatbots, language translation, and text summarization.
Facilitates cross-language communication while translating text or speech from one language to another.
Figuring out whether a text is expressing a positive, negative, or neutral sentiment or emotion.
Recognising and classifying particular things mentioned in the text, such as names, businesses, places, etc.
Helping with content categorization and analysis by locating topics or themes within a collection of documents or text.
Creating a succinct summary of a longer text that highlights its key ideas and pertinent details.
Developing tools that can comprehend questions posed in natural language and respond with relevant data.
Building interactive systems capable of carrying on human-like conversations, offering knowledge, assistance, or support.
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