Named Entity Recognition Expert in Berlin
for accurate language data, matched in minutes with vetted and available freelancersHire experts who classify people, places, organisations and other entities in text, train spaCy or Hugging Face pipelines, and connect extraction to search, analytics and automation workflows. FRATCH matches you quickly with precise, vetted and available freelancers.
Meet FRATCH Experts in Berlin, who have recently used Named Entity Recognition
Hamza K.
Last position:
Academic Research Contributor in Health Sector (Volunteer) at n/a
- Acted as technical consultant to optimize multi-layer ensemble models combining ResNet, CNN-BiGRU-Attention, and XGBoost.
- Guided implementation of a Logistic Regression meta-learner to solve class imbalance problems, achieving 92.86% accuracy and 0.9644 AUC on PTB-XL and Chapman-Shaoxing datasets.
Josphat G.
Last position:
Data Annotation Lead at Sigma AI
- Lead a team of 15 annotators on large-scale computer vision projects for autonomous vehicle systems
- Developed comprehensive annotation guidelines that improved inter-annotator agreement by 35 percent
- Implemented quality control processes that reduced error rates by 42% across all projects
- Collaborated with ML engineers to identify edge cases and improve dataset quality
- Managed annotation projects for Fortune 500 clients, delivering 100% on time
Mathias W.
Last position:
Implementation of an on-premise OCR solution with information extraction at Mindhopper GmbH
- Insurance service provider*
Challenge: Business-critical documents were processed through external OCR providers, with ongoing costs, dependency, and data privacy risks for sensitive insurance data.
Implementation:
- Architecture and production implementation of an on-premise OCR solution with full data ownership
- Methods for recognizing document structures as the basis for automated further processing
- ML-, NLP-, and LLM/VLM-based information extraction, especially from invoices and quotations
Success: Replaced external providers: full data ownership, GDPR-compliant processing, and 75% lower recurring OCR costs per year
Used technologies: Python, Docker, Microservices, FastAPI, PyTorch, Torchvision, MongoDB, MySQL
Kashaf K.
Last position:
AI Consultant / Expert at Siemens Mobility
- Evaluated 45+ AI use cases and developed a prioritization framework for Siemens’ internal AI roadmap.
- Tested internal tools like DRIM, SiemensGPT, Microsoft Copilot; presented evaluation outcomes to stakeholders.
- Identified performance gaps and improved tool adoption by 65%.
- Supported AI knowledge-sharing initiatives, led tool onboarding sessions, and improved team AI literacy.
- Collaborated with engineering, procurement, and digital teams on tool feedback and strategy alignment.
Fiona S.
Last position:
Freelance at Flyworks
Ege P.
Last position:
AI Research Collaborator at NPO
- Contributed to the Karakutu project, developing AI-driven tools to analyze news in Turkey.
- Assisted in web scraping, applied NER for entity extraction, and built interactive filtering interfaces (Vue.js, Plotly.js) for entity and location based search.
- Performed sentiment and content-shift analysis to detect editorial influence in modified news articles.
Sanket T.
Last position:
Master of Engineering: Information and Electrical Engineering at Hochschule Wismar
Discover over 15,000 top freelancers
Statistics of experts using Named Entity Recognition
Aggregated from the professional profiles of matched freelancers.
Experience
10 years
Position duration
2.4 years
Positions per freelancer
8
Top business areas
Information Technology, Research and Development, Product Development
Top industries
Healthcare, Information Technology, Retail
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
100%
Master's degree or higher
86%
Doctorate
14%
Certifications per freelancer
3
Most common languages
German, English, Urdu
Speak two or more languages
100%
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Berlin are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging within that range.
Average rates of experts in Berlin using Named Entity Recognition
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Named Entity Recognition experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Healthcare (86%)
- Information Technology (86%)
- Retail (43%)
- Advertising (29%)
- Education (29%)
- Banking and Finance (29%)
- Insurance (29%)
- Manufacturing (29%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What it does
Named Entity Recognition, commonly called NER, identifies and labels entities in unstructured text. It can distinguish people, organisations, locations, products, dates, medical terms and customer-specific categories. The output turns documents, messages and web content into structured data that other systems can search, analyse or automate.
Where it is used
NER supports information extraction across many types of software and data workflows:
- Extract names, addresses and organisations from business documents
- Enrich search, recommendation and knowledge graph systems
- Route support messages by product, location or account
- Find medical, legal or financial terms in specialist text
- Detect entities in multilingual content and noisy user input
Ecosystem and tooling
Strong implementations often combine Python with spaCy, Hugging Face Transformers, Flair or NLTK. Specialists may use BERT-based models, tokenisation libraries, annotation tools and vector or search systems such as Elasticsearch. Production work also involves model serving, REST APIs, data pipelines, Docker and cloud deployment.
When expertise helps
Companies bring in freelance specialists when entity categories are domain-specific, labelled data is limited or an existing model performs poorly on real documents. Expertise is also useful when a prototype must become a reliable service with monitoring, versioned models and clear handover documentation. In Berlin, projects may involve multilingual products, international teams and text in both German and English.
What strong specialists deliver
A capable professional defines the entity schema before selecting a model. They inspect annotation quality, separate training and evaluation data, and test for spelling variants, abbreviations and ambiguous names. They also measure false positives and missed entities by category, explain trade-offs clearly and build an inference workflow that fits latency, privacy and maintenance requirements.
Collaboration and delivery
NER projects can be delivered remotely when data access, annotation rules and review cycles are well organised. On-site collaboration in Berlin can help when specialists must work closely with legal, medical, editorial or operations teams. Typical deliverables include an annotated dataset, trained pipeline, evaluation report, API integration, deployment setup and guidance for extending entity types safely.
Frequently asked questions
Not sure where to start with Named Entity Recognition? These answers cover the essentials.
Named Entity Recognition finds defined entities in text and assigns labels to them. Companies use it to structure documents, improve search, extract customer or supplier details, support analytics and automate routing or review workflows.
NER identifies the role of a term in context, rather than matching a fixed word or phrase. Keyword search may find every occurrence of “Berlin”, while an NER model can label it as a location and distinguish it from a company or product name in another context.
A strong Named Entity Recognition specialist usually understands text classification, relation extraction, tokenisation and language modelling. Data annotation, Python, spaCy or Hugging Face, API design, search technologies and model monitoring are also valuable for production work.
A focused NER project may need only a small proof of concept, while a production system requires careful schema design, representative annotations and testing across document types. The right level of experience depends on language coverage, domain complexity, data quality, privacy constraints and integration needs.
Named Entity Recognition can support German, English and multilingual workflows when the selected models and training data fit the content. In Berlin, it is useful to check how a specialist handles German compounds, umlauts, local organisation names, mixed-language text and domain-specific terminology.
Remote delivery works well for NER when specialists can access representative data and stakeholders can review annotation guidelines online. On-site work in Berlin may help when sensitive documents, frequent subject-matter reviews or close coordination with local teams are central to the project.
Named Entity Recognition is often easier to control, evaluate and operate for a defined set of labels. Large language models can handle broader instructions and difficult context, but a specialist should assess accuracy, cost, latency, privacy and consistency before choosing one approach or combining both.
Ask a Named Entity Recognition professional to explain their label schema, annotation process and evaluation method. Good specialists discuss false positives and missed entities by category, test on unseen documents, document known limitations and show how the pipeline will be monitored after launch.
The average hourly rate of freelancers in Berlin, Germany who have used Named Entity Recognition in their recent projects is 71 €, which corresponds to a daily rate of about 571 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Named Entity Recognition in their recent projects, 100% hold at least a Bachelor's degree, 86% hold at least a Master's degree, and 14% hold a doctorate.
On average, freelancers in Berlin, Germany who have used Named Entity Recognition in their recent projects have 10 years of professional experience, with a single engagement typically lasting around 2.4 years.
The most common languages among freelancers in Berlin, Germany who have used Named Entity Recognition in their recent projects are German (100%), English (100%), and Urdu (29%).
The most common industries among freelancers in Berlin, Germany who have used Named Entity Recognition in their recent projects are Healthcare (86%), Information Technology (86%), and Retail (43%).
The most common business areas among freelancers in Berlin, Germany who have used Named Entity Recognition in their recent projects are Information Technology (100%), Research and Development (86%), and Product Development (71%).
Main locations of FRATCH Experts, who have recently used Named Entity Recognition
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Countries:
Berlin 
Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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