NLP Cloud vs Tisane Labs
Overview
🔥 Trending in Text & Language Models on StackShare
Detailed Comparison
High performance NLP models based on spaCy and HuggingFace transformers, for NER, sentiment-analysis, classification, summarization, question answering, and POS tagging. All models are production-ready and served through a REST API. You can also deploy your own spaCy models. No DevOps required. | It is NLU with a focus on abusive content. We detect cyberbullying / personal attacks, hate speech, sexual advances, obfuscated profanities, criminal activity, and more. It is trusted by online communities, law enforcement agencies, content filter companies, hate speech researchers, and regtech companies. With the moderation being a controversial topic, it is built from the ground up to be transparent, explainable, and bias-free. We support close to 30 languages, as well as misspelled / purportedly obfuscated content. With Tisane being a complete NLU engine, we also provide the standard text analytics capabilities. |
Sentiment analysis; Classification; Summarization; Question answering; POS tagging; API; Machine learning; AI; Data science | Send text request, receive annotations in
a JSON response; Our API supports more languages than any other vendor. (No, analyzing machine-translated content is not “support”);
Affordable; Simple API;Spot abuse |
Statistics | |
Stacks 8 | Stacks 0 |
Followers 7 | Followers 2 |
Votes 0 | Votes 0 |
Integrations | |
| No integrations available | |
What are some alternatives to NLP Cloud, Tisane Labs?
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.
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
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.
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.
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.
