UBIAI vs rasa NLU
Overview
🔥 Trending in Text & Language Models on StackShare
Detailed Comparison
rasa NLU (Natural Language Understanding) is a tool for intent classification and entity extraction. You can think of rasa NLU as a set of high level APIs for building your own language parser using existing NLP and ML libraries. | It is an efficient and easy-to-use text annotation tool for Natural Language Processing (NLP) applications. With this, you can train an NLP model in few hours by collaborating with team members and using the machine learning auto-annotation feature. |
Open source; NLP; Machine learning | Multi-format document upload: TXT, CSV , JSON , PDF, DOC, HTML;
Multilingual: English, French, German, Arabic, Spanish, etc…;
Dictionary/Regex auto-annotation: input a list of words or regex patterns along with their associated entities. The tool will automatically scan the documents and auto-annotate;
ML auto-annotation: Train an NER model to auto-annotate your documents;
Bias detection: visualize entity and word distribution across your documents to detect skewed annotation toward specific entities.
Collaboration: Share annotation tasks among team members and monitor progress;
Annotation format export: JSON, IOB, Amazon Comprehend, Stanford CoreNLP |
Statistics | |
Stacks 120 | Stacks 2 |
Followers 282 | Followers 11 |
Votes 25 | Votes 0 |
Pros & Cons | |
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What are some alternatives to rasa NLU, UBIAI?
SpaCy
It is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. It comes with pre-trained statistical models and word vectors, and currently supports tokenization for 49+ languages.
Speechly
It can be used to complement any regular touch user interface with a real time voice user interface. It offers real time feedback for faster and more intuitive experience that enables end user to recover from possible errors quickly and with no interruptions.
MonkeyLearn
Turn emails, tweets, surveys or any text into actionable data. Automate business workflows and saveExtract and classify information from text. Integrate with your App within minutes. Get started for free.
Jina
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FastText
It is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices.
Flair
Flair allows you to apply our state-of-the-art natural language processing (NLP) models to your text, such as named entity recognition (NER), part-of-speech tagging (PoS), sense disambiguation and classification.
Taylor AI
It is an API for high-accuracy text classification and entity extraction. We make your unstructured text as easy to work with as your tabular data.
Transformers
It provides general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch.
Gensim
It is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.
Sentence Transformers
It provides an easy method to compute dense vector representations for sentences, paragraphs, and images. The models are based on transformer networks like BERT / RoBERTa / XLM-RoBERTa etc. and achieve state-of-the-art performance in various tasks.
