Are you wondering if it is safe to share your information with Ebbot? Well, you can put your mind at ease now as we reassure you that with Hello Ebbot's special dishwasher, your personal data is completely secure! How does this dishwasher work and how did we make it possible, you may ask? Please keep reading and we will answer all your questions 👀
Recently, one of our clients asked us to teach Ebbot to detect toxic messages in conversations. Thanks to this special request, we got a chance to work on one of the most difficult topics in the Natural Language Processing (NLP) field. And yes, we can't be more excited! 🥳
After successfully extended a powerful Natural Language Processing (NLP) model called "SentenceTransformers" to Swedish language, we decided to continue with another exciting project aiming to half-automate the intent training process by grouping similar sentences.
Implementing a chatbot project can be a long and complex journey if you don't know how to do it. There are many risks associated with automating customer caps, and ultimately the reputation of the organization is at stake, as the chatbot becomes the welcome mat for customers to create that first impression that makes up the majority of what they'll think forever. It's also easy for a chatbots project to drag on, costing the shirt.
We tell you about the customer service employee: your most important resource to influence what customers think of you. You will get 5 tips on how to best increase motivation, performance by creating the right conditions and a sustainable way of working.
Do you know that not every sentence is useful for the learning process? There is information that we do not want Ebbot to memorize, such as phone numbers, emails and spam messages. That is why we decided to build a Machine Learning (ML) model to classify messages as spam or not spam to filter out unnecessary data.
Based on UKPLab's publication, we extended the English SentenceTransformers to a Swedish model using TED2020 parallel sentences dataset and achieved 95.6% accuracy.
Customer service. The priority feature that has gained more and more fire in recent years. 2020 was the year when COVID-19 made it even clearer how important it was to be able to be there for its customers when it mattered. In this context, the ever-progressing technology glacier makes it easier for organizations to offer better service with fewer resources.
Hi everyone, my name is Ebbot. Since I started working here at Hello Ebbot, I have received many questions about how I have the ability to interpret human language as a chatbot. There is no short answer for this question, so I wrote this blog to give you an explanation.
How do you assess a chatbot? It's time to clear up something here we feel. We have seen an increasing number of comments from companies that have tested our customers' chatbots and are unhappy with what they see. Why is that so? What are they looking at? And why can't they show their "official chatbots" leg?