Dasha vs Wit
Overview
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CLI (Node.js)
Manual
Detailed Comparison
Wit enables developers to add a modern natural language interface to their app or device with minimal effort. Precisely, Wit turns sentences into structured information that the app can use. Developers don’t need to worry about Natural Language Processing algorithms, configuration data, performance and tuning. Wit encapsulates all this and lets you focus on the core features of your apps and devices. | Dasha is a conversational AI as a Service platform. Dasha lets you create conversational apps that are more human-like than ever before, quicker than ever before and quickly integrate them into your products. |
Voice-enabled Android and iOS apps;Rasberry Pi based home automation commanded by speech;Google Glass apps accepting voice commands;Robots and drones dialog interfaces (ROS);SMS-based information or remote control services;IM-based information or remote control services;"Quick add" features a la Google Calendar (replacing a form with free text input);Natural Language querying a la Facebook Graph Search (turning a sentence into a database query);Personal Assistants a la Apple’s Siri | Declarative language for conversation design; VSCode extension; native STT, NLP, NLU, NLG and TTS; Support for external TTS; Voice over SIP Trunk; Node.js SDK; Voice over GRPC; Text over GRPC; API-first; Open developer platform; Unlimited conversational depth; High conversational concurrency; Robust digressions and intents for the human-like experience; Custom intents training |
Statistics | |
Stacks 12 | Stacks 3 |
Followers 57 | Followers 8 |
Votes 0 | Votes 0 |
Integrations | |
| No integrations available | |
What are some alternatives to Wit, Dasha?
rasa NLU
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.
NanoNets
Build a custom machine learning model without expertise or large amount of data. Just go to nanonets, upload images, wait for few minutes and integrate nanonets API to your application.
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.
Inferrd
It is the easiest way to deploy Machine Learning models. Start deploying Tensorflow, Scikit, Keras and spaCy straight from your notebook with just one extra line.
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.
GraphLab Create
Building an intelligent, predictive application involves iterating over multiple steps: cleaning the data, developing features, training a model, and creating and maintaining a predictive service. GraphLab Create does all of this in one platform. It is easy to use, fast, and powerful.
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
It is geared towards building search systems for any kind of data, including text, images, audio, video and many more. With the modular design & multi-layer abstraction, you can leverage the efficient patterns to build the system by parts, or chaining them into a Flow for an end-to-end experience.
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.
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.
