Integritet och anonymitet står i centrum i Hello Ebbots projekt med Dynamic Code.
Som en av de ledande truckleverantörerna i världen har Jungheinrich ett brett utbud truckar.
Läs mer om hur vi framgångsrikt effektiviserat serviceavdelningar i offentlig sektor!
Säg hej till Jeanett! NetOnNets digitala medarbetare som hanterar köpresan från början till slut.
Elgot är Öresundskrafts digitala medarbetare som revolutionerar sättet energibranschen hanterar kundresan
After one month of preparing the dataset and training, we proudly present to you a T5 (Text-To-Text Transfer Transfromer) based model that paraphrases any questions in the Swedish language. By learning from paraphrased questions by the Swedish T5, we are no longer limited to just the topics in our current questions database.
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 not 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.
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.
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.
In this blog, the NLP team at Hello Ebbot will share all the open-source resources (including models, tools and datasets) that we found specifically serves Swedish – the language that our colleague Ebbot speaks mainly.
A sentiment analysis model with 75% average accuracy specifically trained by Hello Ebbot to classify text data in Swedish.