best model for named entity recognition - Brave Search

huggingface.co › dslim › bert-base-NER

dslim/bert-base-NER · Hugging Face

bert-base-NER is a fine-tuned BERT model that is ready to use for Named Entity Recognition and achieves state-of-the-art performance for the NER task.

edenai.co › post › top-10-named-entity-recognition-ner-api

Top 10 Named Entity Recognition (NER) API

It features state-of-the-art speed and neural network models for tagging, parsing, named entity recognition, text classification and more, multi-task learning with pre-trained transformers like BERT, as well as a production-ready training system and easy model packaging, deployment and workflow ...

Videos

GLiNER - Best Model for Named Entity Recognition - Run in Google ...

GLiNER: Easiest way to do Entity Extraction in 2024? - YouTube

Best way to do Named Entity Recognition in 2024 with GliNER and ...

Named entity recognition (NER) model evaluation - YouTube

Named Entity Recognition (NER): NLP Tutorial For Beginners - S1 ...

NERD: Better Named Entity Recognition and Detection - YouTube

February 1, 2022

assemblyai.com › blog › 6-best-named-entity-recognition-apis-entity-detection

6 best named entity recognition APIs for entity detection

Ontology-based Named Entity Recognition uses knowledge-based recognition that relies on predefined lists and rules. For instance, it might have a database of company names, a list of common first and last names, or geographic locations.

medium.com › quantrium-tech › top-3-packages-for-named-entity-recognition-e9e14f6f0a2a

Top 3 Packages for Named Entity Recognition | by Maria Philna Aruja | Quantrium.ai | Medium

February 12, 2022 - QUANTRIUM GUIDES Top 3 Packages for Named Entity Recognition Comparing SpaCy, NLTK and Flair — the top 3 NER models By the time you finish reading this article, the amount of text on the internet …

kairntech.com › home › blog › the complete guide to named entity recognition (ner): methods, tools, and use cases

The Complete Guide to Named Entity Recognition (NER) - Kairntech

June 3, 2025 - ... BERT is a large language model that can be fine-tuned for NER tasks. NER, on the other hand, is a goal — extracting entities — which BERT can help achieve when integrated into a recognition pipeline.

Papers with Code

paperswithcode.com › task › named-entity-recognition-ner

Named Entity Recognition (NER)

MinerU2.5, a 1.2B-parameter document parsing vision-language model, achieves state-of-the-art recognition accuracy with computational efficiency through a coarse-to-fine parsing strategy.

edenai.co › post › best-named-entity-recognition-apis

Best Named Entity Recognition APIs in 2025 | Eden AI

NLP Cloud's NER API offers advanced entity recognition with customization options, multilingual support, and pre-trained models for accurate extraction of names, locations, organizations, and more.

reddit.com › r/machinelearning › [d] named entity recognition (ner) libraries

r/MachineLearning on Reddit: [D] Named Entity Recognition (NER) Libraries

January 7, 2023 -

Hi everyone, I have to cluster a large chunk of textual conversational business data to find relevant topics in it.

Since there is lot of abstract info in every text like phone, url, numbers, email, name, etc., I have done some basic NER using regex and spacy NER to tag such info and make the texts more generic and canonicalized.

But there are some things like product names, raw materials, brand/model, company, etc. which couldn't be tagged. Also, the accuracy of regex and spacy NER isn't high enough.

Can anyone suggest a good python NER library, which is accurate and fast enough, preferably has pre-trained models and can tag diverse fields.

Thanks.

If spaCy’s NER isn’t picking up what you need, you’ll probably need to look into creating your own annotations and fine tuning a model or training a custom model. It isn’t too hard using BIO/BILOU tags. Things like “raw materials” and particularly niche models and brands are unlikely to be picked up by off the shelf solutions.

In 2024 the best solution in order to perform NER on any sort of tag without data labelling it to use a generative model. For example you could load a relatively small generative model on your machine like Phi 3 with Ollama and come up with the right prompt to extract the right entities. You could also simply plug into an AI API like NLP Cloud's NER API . In general, the bigger the model, the better. But of course you have to carefully watch costs...

reddit.com › r/languagetechnology › best ner models?

r/LanguageTechnology on Reddit: Best NER Models?

March 26, 2025 -

Hi, I’m new to this field. Do you have suggestions for NER models?

I am currently using spacy but I find it challenging to finetune it. Is this normal?

Do you have any suggestions? Thank you!

A finetuned spacy model works quite well for me IF it is properly annotated and annotation is a struggle if you don't use some labelling app. In my case I use confidential docs so I prefer not to use an external annotation app. Something that works for me is exporting your text to excel, copy paste NER ents you want your models to filter out in a second column and define the ent type in a third column. Then I run a python script to concatenate that information (by using string index matching) into the spacy required format for training. Still a bit of a hassle but I'm seeing results.

GLiNER "GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios." https://huggingface.co/urchade/gliner_medium-v2.1 https://github.com/urchade/GLiNER

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github.com › urchade › GLiNER

GitHub - urchade/GLiNER: Generalist and Lightweight Model for Named Entity Recognition (Extract any entity types from texts) @ NAACL 2024

""" # Labels for entity prediction # Most GLiNER models should work best when entity types are in lower case or title case labels = ["Person", "Award", "Date", "Competitions", "Teams"] # Perform entity prediction entities = model.predict_en...

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restack.io › p › entity-recognition-best-models-answer-cat-ai

Best Models For Entity Recognition | Restackio

... Knowledge AI: Fine-tuning NLP Models for Facilitating Scientific Knowledge Extraction and Understanding · Fine-tuning pre-trained language models (PLMs) for Named Entity Recognition (NER) involves adapting models like BERT, SciBERT, and SciDeBERTa to effectively identify and classify entities ...

turing.com › kb › a-comprehensive-guide-to-named-entity-recognition

A Comprehensive Guide to Named Entity Recognition (NER)

This will help improve accuracy and make the model more generic for other datasets. Next, pick any algorithm like BERT or spaCy, which is an open-source NLP library for advanced NLP tasks. Try these algorithms and evaluate what works best for your model. Q. What are the named entity recognition ...

restack.io › p › entity-recognition-answer-best-llm-named-entity-recognition-cat-ai

Best Llm For Named Entity Recognition | Restackio

Large Language Models (LLMs) have revolutionized Named Entity Recognition (NER) by providing advanced capabilities that enhance the extraction of entities from text. The best LLMs for named entity recognition, such as BERT, GPT-3, and Llama-3, leverage their transformer architectures to understand ...

arxiv.org › html › 2402.17447v1

Deep Learning Based Named Entity Recognition Models for Recipes

February 27, 2024 - We conclude that few-shot prompting on LLMs has abysmal performance, whereas the fine-tuned spaCy-transformer emerges as the best model with macro-F1 scores of 95.9%, 96.04%, and 95.71% for the manually-annotated, augmented, and machine-annotated datasets, respectively.

BioMedical Engineering OnLine

biomedical-engineering-online.biomedcentral.com › articles › 10.1186 › s12938-018-0573-6

Comparison of named entity recognition methodologies in biomedical documents | BioMedical Engineering OnLine | Full Text

November 6, 2018 - Biomedical named entity recognition (Bio-NER) is a fundamental task in handling biomedical text terms, such as RNA, protein, cell type, cell line, and DNA. Bio-NER is one of the most elementary and core tasks in biomedical knowledge discovery from texts. The system described here is developed by using the BioNLP/NLPBA 2004 shared task.

quora.com › What-is-the-best-algorithm-for-named-entity-recognition-How-hard-is-it-to-build-this-tool

What is the best algorithm for named entity recognition? How hard is it to build this tool? - Quora

I presume that the best one depends on the data you have trained the model with and how well you have implemented that algorithm. The most popular technique for NER is Conditional Random Fields. But recently with the ...

VSoft Consulting

blog.vsoftconsulting.com › blog › understanding-named-entity-recognition-pre-trained-models

Understanding Named Entity Recognition (NER) Pre-Trained Models

May 9, 2025 - Stanford NER stated that Conditional ... future observations while learning a new pattern. This combines the best of HMM (Hidden Markov Model) and MEMM (Maximum Entropy Markov Model). In terms of performance, it is one the best ...

en.wikipedia.org › wiki › Named-entity_recognition

Named-entity recognition - Wikipedia

September 22, 2025 - As of 2007, state-of-the-art NER systems for English produce near-human performance. For example, the best system entering MUC-7 scored 93.39% of F-measure while human annotators scored 97.60% and 96.95%. Despite high F1 numbers reported on the MUC-7 dataset, the problem of named-entity recognition is far from being solved.

arxiv.org › html › 2401.10825v1

A survey on recent advances in Named Entity Recognition

January 19, 2024 - One of the findings in (Lample et al., 2016) is that recurrent models tend to favor later entries and that the resulting feature vectors encode suffixes more than they encode prefixes. For this reason, the authors of the cited work recommend Bi-LSTM to better capture the prefix. To improve entity recognition, context encoding is a crucial aspect of NER systems aiming at capturing the contextual information of words in a sentence.

sunscrapers.com › blog › named-entity-recognition-comparison-spacy-chatgpt-bard-llama2

Named Entity Recognition - Comparison of SpaCy, ChatGPT, Bard and Llama2

Both GPT models returned the best result with one name missing (different for both), however, with bonus points for additional detailed information about listed organizations. The second place belongs to the Llama2 model with one missing name ...