CMC Global’s software development service is the key differentiator for your business to keep pace with ever-changing infrastructure and IT requirements without any third party.
CMC Chatbot is capable of scaling, ready to serve new customers. The system automatically answers messages anytime, anywhere, maximizing customer experience with the business.
CMC Chatbot supports 24/7 and accurate responses to increase customer satisfaction.
CMC Chatbot helps to free up human resources so that your business can focus on core activities that directly generate profits.
CMC Chatbot helps businesses reduce costs up to 80% by automating the entire process and releasing resources.
It includes pre-training language models, pre-trained word vectors such as Hugging Face’s Transformers library. It applies language models with specific tokenization and futurization to compute sequence and sentence level representations for each example in the training data. In addition, the tokenizers can be supported in splitting text into both tokens and multiple labels.
A tool for intent classification, response retrieval and entity extraction in chatbots. This will look for NLU training data files, train data and store a trained model in the models directory.
Dual Intent Entity Transformer is state-of-the-art in NLP technology. It is a multi-task transformer architecture that handles both intent classification and entity recognition together. It provides the ability to plug and play various pre-trained embeddings like BERT, GloVe, ConveRT, and so on. There isn’t a single set of embeddings that is consistently the best across different datasets.
The module supports smart analytic functions such as sentiment analysis, behavior analysis, recommendation, etc.
It includes pre-training language models, pre-trained word vectors such as Hugging Face’s Transformers library. It applies language models with specific tokenization and futurization to compute sequence and sentence level representations for each example in the training data. In addition, the tokenizers can be supported in splitting text into both tokens and multiple labels.
