Offline Text Entity Extraction Model

@kamran2012

Hello there, I will deliver a fine-tuned NER model — packaged with all weights, embeddings, and tokenizer files for fully offline inference on both Windows and Linux — along with a CLI script that processes a folder of text files and outputs extracted entities in JSON or CSV. For the architecture, I will use a distilled transformer (such as DistilBERT) fine-tuned on your corpus. This keeps the total footprint well under 500 MB while hitting strong F1 scores. I will also structure the label schema so adding entity types like persons, orgs, or dates later requires only a config change and retraining — no architectural rework. Questions: 1) Roughly how large is your training corpus, and is it already annotated in IOB/BIO format? Looking forward to potentially working together. Thanks, Kamran

@shadabkhan92

I have done similar tasks, using NLTK, Spacy etc with local llms also lets discuss it over chat will show some demos

@mzdesmag

Hello sir, Did go through your job description and glad to share that I have enormous experience in working with Offline Text Entity Extraction Model I'm a seasoned programmer and Engineer with quality experience in Flutter, React, Node.JS, SpringBoot, Frontend and Backend Development, Python, Matlab, R studio, C, C++, C#, OpenCV, OpenGL, Tesseract OCR, google vision, Statistical programming/R progamming data analysis Computing for Data Analysis Time Series & Econometric, Machine learning, AI, Deep learning, Matlab and Mathematica, 3D modeling, CAD/CAM,AutoCAD, 2D, Architectural Engineering, SolidWorks, Unity 3D, PCB, Electronics, Arduino, Automation, Embedded and Firmware , IOT, Electrical/Mechanical Engineering I am a TOP Rated Freelancer, and you can check my reviews here as well: https://www.freelancer.com/u/mzdesmag. Looking forward to potentially working together on this project. Thanks and Best regards, Adekunle.

@webmasters486

Hi there, I’ve carefully reviewed your project requirements related to data processing, and I’m confident that my expertise in developing efficient data pipelines and automating workflows will help achieve your goals. With extensive experience in data extraction, mining, and processing using tools like Pandas, I can streamline and optimize your data operations for maximum efficiency. I’d love the opportunity to discuss how I can contribute to the success of your project. Feel free to check out my portfolio for examples of my work: Portfolio: https://www.freelancer.com/u/webmasters486/AI-automation Looking forward to your response! Best regards, Muhammad Adil

@UzeeTech

Utilizing my extensive 7+ years of experience as a Full-Stack Developer and my expertise in Python, I am confident that I can deliver a solution that exceeds your expectations for the Offline Text Entity Extraction Model. Having built numerous AI-powered solutions, including NLP models like Named Entity Recognition systems, I'm well-versed with frameworks you mentioned such as spaCy and Hugging Face Transformers. My approach is always holistic and oriented towards building end-to-end solutions. From training and fine-tuning an entity recognition architecture to validating it rigorously on diverse datasets, I will ensure the final model has a minimum F1 score of 0.85 for the "Data" entities—optimizing its performance to meet your inference latency and memory footprint necessities. Additionally, I emphasize meticulous handover with concise yet comprehensive documentation and clean coding practices. These commitments would enable you to tweak and improve the model's efficiency in the absence of the internet without any hassle; a skill I know is vital for your project. Reach out to me now, let's develop something solid

@devanshkhandekar

Hi,I am a seasoned Applied ML Engineer(6+ yoe) & I can build this as a fully offline,self-contained NER/entity extraction system My approach: -First,I will define the exact meaning of the target “Data” entity & prepare a clean BIO-style annotation format for training & validation -I will benchmark 2 practical offline approaches: 1.GLiNER-based extraction:useful for flexible entity definitions & future expansion to people,dates & custom business entities 2.Fine-tuned Transformer NER:using a lightweight model such as DistilBERT/MiniLM for strong accuracy,then optimizing it with ONNX/quantization for fast CPU inference -Add a rule-based post-processing layer for high-precision patterns such as file names,table names,field names,dataset references & known lookup terms -The final deliverable will include an offline inference script/CLI that accepts a folder of text files & outputs extracted entities in JSON/CSV -Validate the model on a held-out set & provide F1,precision,recall,latency,model size,training parameters & setup instructions for Windows/Linux Relevant Experience: -Biomedical NLP:Developed entity extraction,linking & domain-specific information retrieval systems for pharmaceutical & medical documentation -Document Intelligence:Built production-ready RAG pipelines & semantic search workflows for unstructured text I will conduct a benchmarking phase to compare GLiNER against fine-tuned Transformer NER,packaging the optimal model into a lightweight,offline solution

@modesorehari6

Hello, I can build a fully offline, production-ready Named Entity Recognition (NER) system tailored to your “Data” entity extraction needs, meeting both accuracy and performance constraints. **Approach:** I’ll fine-tune a lightweight Transformer (e.g., DistilBERT) or optimized spaCy pipeline depending on your dataset size and latency targets. The model will be trained on your corpus, validated properly, and optimized to achieve ≥0.85 F1 while keeping inference fast (<500 ms/doc on CPU) and total footprint under 500 MB. **Key deliverables:** • Trained offline NER model (no external dependencies) • CLI/Python tool to process folders of text → JSON/CSV output • Cross-platform support (Windows & Linux) • Clean, well-documented code for future retraining/extension • Evaluation report (F1, precision/recall, training setup) **Optimization:** Model compression (quantization/pruning if needed) and efficient tokenization to ensure speed and size constraints are met without sacrificing accuracy. **Flexibility:** Architecture will support easy retraining to extend entities (people, orgs, dates, etc.) with minimal changes. **Timeline:** 10–14 days **Budget:** ₹75,000 INR If needed, I can also include a small annotation/validation helper to streamline future dataset updates. Ready to start as soon as you share the corpus.

@Vsion2

Welcome to professional Python development services! Hi there, I'm Alema, a Python expert programmer who strives for clear code in atmospheric, numerical weather prediction, physics, and all other seminal fields. I'm ready to provide you with high-quality services. I have completed 350+ projects with a 100% Positive Rating. If you are looking for Quality work, look no further. Also, we are a team of professional workers, and we are always available 24/7 to help employers without limitations, and delivery is guaranteed on time. Your faithfully. Eng. Alema Akter

@Visharaa2024

As an experienced freelancer with a demonstrated proficiency in Python and Software Architecture, I'm perfectly suited for your Offline Text Entity Extraction Model project. With over 8+ years of solid problem-solving experience and a strong background in AI implementation, I have a deep understanding of the technical requirements at hand. Throughout my career, I've developed, fine-tuned, and delivered high-performing models using open-source technologies such as spaCy or Hugging Face Transformers - the architecture you've mentioned. In terms of performance and efficiency, I assure you that your project is in good hands. From training the model on your provided corpus to validating and packaging it for native use on Windows and Linux without relying on external APIs - I'll make sure the inference latency fits well under 500ms per document and keeps the model footprint below 500MB. Furthermore, I aim to deliver far beyond your immediate requirements. Though we're starting with "Data" entities, my thorough understanding of NLP practises means I’ll design an architecture that remains flexible enough to facilitate expansion towards people's names, organizations, or even dates and times. I will provide concise setup instructions, clear code comments - ensuring future tweaks can be made without needing internet access. To sum it all up, my 100+ successful projects stand as a testament to my expertise in delivering what is needed when it's needed. Let's get started!!

@vasylklymiu

Hello. I read through your project description. Training a model that runs entirely offline is actually more fun than using cloud APIs because you never get surprise bills or rate limits. I have learned that keeping the model under 500MB means choosing a smaller transformer like DistilBERT or even a spaCy pipeline instead of the giant ones. The validation set you provide will be the only thing that tells me if the model is ready or needs more tuning. I will use spaCy with a transformer or a BiLSTM tagger depending on your document length. The inference script will take a folder and output JSON with clear comments for your future changes. The final model package will include all embeddings and lookup tables so nothing calls home. Send over your corpus and a list of the Data entities and I can train the first version within a few days.

@ydvhitender

With 6+ years of experience in AI and automation, I can build an accurate and efficient text entity extraction model tailored to your offline needs. I have strong experience in NLP and machine learning, working with tools like spaCy and Hugging Face Transformers to deliver reliable solutions. I understand the need for a fully offline system, so the model will run independently without external APIs or downloads during inference. I’ve also built CLI tools and Python utilities that are easy to use and provide clean outputs in JSON or CSV formats. While your current focus is on “Data” entities, I’ll design the model to be scalable, allowing you to expand it later to include entities like names, organizations, or dates with minimal retraining. I focus on building practical, efficient systems that perform well in real-world use. If this fits your needs, message me with “Hi Hitender” and we can discuss further.

@Ankurhardia34

Hello, I’m Ankur Hardiya, a friendly freelance developer with an awesome team. I read your requirement for Text-Entity model and I’m super excited to develop and design a fantastic Android and iOS application for you. With my experience in Flutter and native languages, I can build high-performing apps for both Android and iOS platforms. Whether you need an eCommerce app, a custom business app, or anything in between, I can deliver: * Native app experience for optimal performance * User-friendly interface and intuitive navigation * Seamless integration with backend systems * Ongoing maintenance and updates I’m passionate about creating mobile apps that make a difference, and I’m eager to discuss your project in detail. Thanks a bunch for thinking of me for your project. I’m all set to turn your ideas into something amazing in today’s competitive world. Regards, Ankur Hardiya

@susany6

Hello, This is a well-defined and technically interesting project, and I’d be glad to help you build a fully offline, high-performance NER solution. I have experience working with spaCy and transformer-based models, including fine-tuning, evaluation, and packaging models for local, dependency-free inference. I can design a lightweight yet accurate pipeline tailored to your “Data” entities, ensuring it meets your F1, latency, and size constraints. The final deliverable will include a fully self-contained model, a simple CLI/Python script for batch processing, and clean, well-commented code for future retraining or extension. I’ll also provide clear documentation and a concise report covering training setup, evaluation metrics, and optimization decisions. The architecture will be flexible so you can easily expand it to additional entity types later. Looking forward to collaborating and delivering a reliable offline solution. Best regards,

@imtiazc2

Hi, I’m very interested in helping you build a fully offline, self-contained text-processing model for accurate “Data” entity extraction. I have hands-on experience designing and deploying Named Entity Recognition systems using spaCy and Hugging Face Transformers, with a strong focus on performance, portability, and clean architecture. Your requirement for a completely offline solution with strict latency and size constraints aligns well with how I typically structure production-ready NLP pipelines. Here’s how I’ll approach your project: * **Model Design & Training**: I’ll fine-tune a lightweight transformer (such as DistilBERT) or an optimized spaCy pipeline depending on your dataset size and performance targets, ensuring we hit the ≥0.85 F1 score. * **Performance Optimization**: I’ll keep inference under 500 ms per document by optimizing tokenization, batching (if needed), and model size—while ensuring the full package stays under 500 MB. * **Offline Packaging**: All dependencies, embeddings, and resources will be bundled locally so the system runs

@kausarafridi07

As the co-founder of MTAI Software Labs, I lead a team of highly skilled developers and AI experts who are well-versed in delivering enterprise-grade solutions, just like the one you're seeking. We specialize in NER and AI applications, making us a perfect match for your project. Our core expertise lies in Python, which is essential for developing high-performing NER algorithms. Combining that with our knowledge of popular frameworks like spaCy and Hugging Face Transformers puts us in an ideal position to cater to your needs. Let me assure you that we'll deliver a comprehensive solution following your acceptance criteria diligently. By training the entity extraction model on the corpus you provide and validating it on a held-out set, we can develop a model tailored precisely to your specifications. And not only do we focus on the immediate project, but we're also mindful of future requirements. Hence, we'll design your model that remains flexible enough to be expanded later on-demand. Moreover, having executed projects with stringent offline requirements in the past, we understand the value of self-contained models and can optimize for low latency and minimal footprint. Our commitment to long-term partnership ensures post-project support including modifications made without relying on live internet connections. Finally, to demonstrate complete transparency with our development process,

@devu58

Hey, I would be happy to work in your offline Named Entity Recognition (NER) system capable of accurately extracting your specified “Data” entities from raw documents. With a strong background in Artificial Intelligence and Natural Language Processing, along with experience developing custom machine learning pipelines, I can design a reliable and efficient solution that meets all of your technical requirements. For this project, I propose developing a fine-tuned transformer-based NER model using frameworks such as Hugging Face Transformers or spaCy, depending on what best fits your dataset size, performance needs, and model footprint requirements. The model will be trained exclusively on the dataset you provide and validated on a held-out dataset to ensure robust generalization. The final system will be completely self-contained and capable of running offline, with all dependencies, embedding files, and resources packaged locally. I will ensure the model meets your specified criteria, including an F1 score of at least 0.85, inference latency below 500 ms per document on CPU, and a total footprint under 500 MB.

@hockfx

You need a fully self-contained text-processing model that can run offline to extract specific 'Data' entities from raw documents. Model architecture: I will design or fine-tune a Named Entity Recognition model using spaCy or Hugging Face Transformers, ensuring it accurately identifies the specified entities with an F1 score of at least 0.85. Training and validation: The model will be trained on the corpus you provide and validated on a held-out set to ensure performance. Deployment: I will deliver a packaged version that runs on both Windows and Linux, including a lightweight CLI or Python script that processes a folder of text files and outputs extracted entities in JSON or CSV format. Documentation: Clear setup instructions and comments within the code will be provided for future modifications without internet access. Timeline: 8 days.

@somendersingh96

Hello, I can build a fully offline NER system that accurately extracts your “Data” entities and runs seamlessly on both Windows and Linux without any external dependencies. ✅ Approach • Select the best architecture (spaCy or lightweight Transformer) based on accuracy vs. footprint constraints • Fine-tune the model on your dataset with a proper train/validation split • Optimize for F1 ≥ 0.85 while keeping model size and latency within limits • Package everything for fully offline inference (no API calls, all assets local) • Design the system to be easily extendable for new entity types later ✅ Implementation • Custom NER training pipeline with evaluation metrics • Optimized inference (CPU-friendly, <500ms per document) • Config-driven setup for easy retraining and updates ✅ Deliverables • Trained offline model (≤500MB total footprint) • CLI / Python script to process folders → JSON/CSV output • Clean, well-commented codebase • Setup guide for Windows & Linux (no internet required) • Short report with training details, metrics, and improvement notes ✅ Key Focus • Accuracy + efficiency balance • Clean, maintainable architecture • Future scalability for additional entity types I’ve worked with NER systems and NLP pipelines, and I’ll ensure this solution is robust, fast, and completely self-contained. Ready to review your dataset and start training immediately. Best regards, Somender Singh

@shashankpetkar

Hello, I hope you’re doing well. I’ve reviewed your requirement for a fully offline text-processing model, and I’d be happy to help deliver a robust and efficient solution. Your need for zero internet dependency, along with performance and size constraints, is clear and achievable. I propose building a Named Entity Recognition (NER) model using a reliable open-source framework such as spaCy or Hugging Face Transformers. The model will be trained on your provided dataset and validated to achieve the target F1 score of at least 0.85. I will also optimize it to ensure inference latency remains under 500 ms per document and the total footprint stays within 500 MB. You will receive a packaged, ready-to-run solution compatible with both Windows and Linux, along with a simple CLI or Python script to process text files and output results in JSON or CSV format. I will include clear setup instructions and well-commented code to ensure easy maintenance and future enhancements. The architecture will be designed to allow easy expansion for additional entity types such as people, organizations, or dates. I’d be glad to discuss your dataset and any specific requirements further. Best regards, Shashank