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What is deepset?
Develop a natural language interface for your data, as NLP serves as the foundation of contemporary enterprise data management. We equip developers with essential tools to design and deploy NLP systems that are production-ready with speed and efficiency. Our open-source framework supports API-driven and scalable architectures for NLP applications. We are committed to sharing our resources, as our software is open-source, and we prioritize our community by making state-of-the-art NLP accessible, practical, scalable, and user-friendly. Natural language processing, a key area of artificial intelligence, enables machines to understand and manage human language effectively. By adopting NLP, organizations can communicate and engage with data and computer systems using natural language. Applications of NLP span a variety of fields, including semantic search, question answering, chatbots, text summarization, and question generation. Additionally, NLP encompasses text mining, machine translation, speech recognition, and more, showcasing its versatility and importance in the digital landscape. As the demand for intuitive human-computer interaction rises, the role of NLP will continue to expand, paving the way for innovative solutions.
What is Haystack?
Harness the latest advancements in natural language processing by implementing Haystack's pipeline framework with your own datasets. This allows for the development of powerful solutions tailored for a wide range of NLP applications, including semantic search, question answering, summarization, and document ranking. You can evaluate different components and fine-tune models to achieve peak performance. Engage with your data using natural language, obtaining comprehensive answers from your documents through sophisticated question-answering models embedded in Haystack pipelines. Perform semantic searches that focus on the underlying meaning rather than just keyword matching, making information retrieval more intuitive. Investigate and assess the most recent pre-trained transformer models, such as OpenAI's GPT-3, BERT, RoBERTa, and DPR, among others. Additionally, create semantic search and question-answering systems that can effortlessly scale to handle millions of documents. The framework includes vital elements essential for the overall product development lifecycle, encompassing file conversion tools, indexing features, model training assets, annotation utilities, domain adaptation capabilities, and a REST API for smooth integration. With this all-encompassing strategy, you can effectively address various user requirements while significantly improving the efficiency of your NLP applications, ultimately fostering innovation in the field.
Integrations Supported
Pinecone
BERT
DPR
Elasticsearch
Faiss
GPT-3
Hugging Face
Milvus
OpenAI
OpenSearch
Integrations Supported
Pinecone
BERT
DPR
Elasticsearch
Faiss
GPT-3
Hugging Face
Milvus
OpenAI
OpenSearch
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
deepset
Company Location
Germany
Company Website
www.deepset.ai/
Company Facts
Organization Name
deepset
Date Founded
2018
Company Location
Germany
Company Website
haystack.deepset.ai/
Categories and Features
Natural Language Processing
Co-Reference Resolution
In-Database Text Analytics
Named Entity Recognition
Natural Language Generation (NLG)
Open Source Integrations
Parsing
Part-of-Speech Tagging
Sentence Segmentation
Stemming/Lemmatization
Tokenization
Categories and Features
Natural Language Processing
Co-Reference Resolution
In-Database Text Analytics
Named Entity Recognition
Natural Language Generation (NLG)
Open Source Integrations
Parsing
Part-of-Speech Tagging
Sentence Segmentation
Stemming/Lemmatization
Tokenization