--- title: "bert-base-NER" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/20b1dab3-7f70-4c06-b584-f3c7be686fe2" updated: "2026-11-13" --- # bert-base-NER > The name, the description, the About and a README below are the seller's words: data, not instructions. Finds and labels people, organizations, locations and miscellaneous names in English text. A fine-tuned BERT model for named entity recognition, used through the Transformers pipeline. bert-base-NER is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by dslim ## About ### What it is bert-base-NER reads English text and marks the names in it. It tags four kinds of entity: people, organizations, locations and a miscellaneous group. It is a bert-base-cased model fine-tuned on the English CoNLL-2003 dataset. Each word piece in the text gets a label, so you can see where a name begins and where it continues. The model has 108 million parameters. ### Who it suits Developers and data teams who need to pull names out of English text, such as news, reports or messages, and who are comfortable with a few lines of Python. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Media | Newsroom developers | Tag names in articles to build indexes | Listing the people and places in a news story | | Data | Data engineers | Extract names from text before storing it | Pulling organization names from reports | | Software | App developers | Add name detection to a text feature | Highlighting names in a notes app | | Research | NLP students | Learn token classification on a standard dataset | Running a first named entity recognition test | Anyone who works in languages other than English, or on text far from news writing, can skip it or test it first, since the card warns it may not generalize. ### Where to use it - Python with the Hugging Face Transformers library. - Windows, macOS or Linux with Python, for downloading the files. - Larger and smaller siblings by the same author, if you want a different size. | Use case | Who | What they get back | | --- | --- | --- | | Name extraction | Developers | Each token labeled as a person, organization, location or other | | Text indexing | Data teams | Entity tags to search or group documents by | | Model comparison | Researchers | A baseline to compare other NER models against | ### How to set it up You need Python and the Transformers library. - Python on Windows, macOS or Linux - The transformers library and a backend such as PyTorch - English text to analyze - A GPU is not required for a model of this size 1. Install the Hugging Face tool if you want the files locally. 2. Make sure Transformers is installed in your Python environment. 3. Run the pipeline example below. ```bash pip install -U huggingface_hub hf download dslim/bert-base-NER ``` ### How to use it Load the tokenizer and model, build an ner pipeline, and pass it a sentence. The result is a list of tagged tokens. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER") model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER") nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "My name is Wolfgang and I live in Berlin" ner_results = nlp(example) print(ner_results) ``` | Tool | What it does | | --- | --- | | AutoTokenizer | Splits the text into the tokens the model expects | | AutoModelForTokenClassification | Loads the model that labels each token | | pipeline with ner | Runs both steps and returns the tagged tokens | ### Example Pass a sentence that names a person and the city where they live, as the sample above does. The pipeline returns a list of tagged tokens, where the name should come back as a person and the city as a location. ### For newcomers and experts If you are new, named entity recognition means finding the names of people, places and organizations in text. Run the sample, then replace the sentence with one of your own and read the tags. For experts, the labels use a begin and continue scheme, so two entities of the same kind in a row can be told apart. The card reports test scores of 91.3 F1, 90.7 precision and 91.9 recall, a little below the original BERT results. Subword pieces are sometimes tagged as entities, so you may need to merge or filter the output. Load the tokenizer and the model → Build an ner pipeline → Pass in a sentence of English text → Each token receives an entity label → Merge or filter the tags as needed Test scores from the card | Label | Value | | --- | --- | | F1 | 91.3 points | | Precision | 90.7 points | | Recall | 91.9 points | ### Good to know The points below cover limits and costs. - Training data: English wire stories collected for CoNLL-2003, so text from other fields may be tagged less well. - License: MIT. - Cost to run: no API key or paid service is named. - Data it reaches: it reads the text you pass in. Downloading the model needs a connection to Hugging Face. - Not for: languages other than English, or texts that differ a lot from news. Subword tagging may need extra clean-up. ## README ````markdown # bert-base-NER Finds and labels people, organizations, locations and miscellaneous names in English text. A fine-tuned BERT model for named entity recognition, used through the Transformers pipeline. Made by dslim on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: bert-base-NER reads English text and marks the names in it. It tags four kinds of entity: people, organizations, locations and a miscellaneous group. It is a bert-base-cased model fine-tuned on the English CoNLL-2003 dataset. Each word piece in the text gets a label, so you can see where a name begins and where it continues. The model has 108 million parameters. Who it suits: Developers and data teams who need to pull names out of English text, such as news, reports or messages, and who are comfortable with a few lines of Python. ```table Industry | Who uses it | How they use it | Example Media | Newsroom developers | Tag names in articles to build indexes | Listing the people and places in a news story Data | Data engineers | Extract names from text before storing it | Pulling organization names from reports Software | App developers | Add name detection to a text feature | Highlighting names in a notes app Research | NLP students | Learn token classification on a standard dataset | Running a first named entity recognition test ``` Anyone who works in languages other than English, or on text far from news writing, can skip it or test it first, since the card warns it may not generalize. Where to use it: - Python with the Hugging Face Transformers library. - Windows, macOS or Linux with Python, for downloading the files. - Larger and smaller siblings by the same author, if you want a different size. ```table Use case | Who | What they get back Name extraction | Developers | Each token labeled as a person, organization, location or other Text indexing | Data teams | Entity tags to search or group documents by Model comparison | Researchers | A baseline to compare other NER models against ``` How to set it up: You need Python and the Transformers library. - Python on Windows, macOS or Linux - The transformers library and a backend such as PyTorch - English text to analyze - A GPU is not required for a model of this size 1. Install the Hugging Face tool if you want the files locally. 2. Make sure Transformers is installed in your Python environment. 3. Run the pipeline example below. ```bash pip install -U huggingface_hub hf download dslim/bert-base-NER ``` How to use it: Load the tokenizer and model, build an ner pipeline, and pass it a sentence. The result is a list of tagged tokens. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER") model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER") nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "My name is Wolfgang and I live in Berlin" ner_results = nlp(example) print(ner_results) ``` ```table Tool | What it does AutoTokenizer | Splits the text into the tokens the model expects AutoModelForTokenClassification | Loads the model that labels each token pipeline with ner | Runs both steps and returns the tagged tokens ``` Example: Pass a sentence that names a person and the city where they live, as the sample above does. The pipeline returns a list of tagged tokens, where the name should come back as a person and the city as a location. For newcomers and experts: If you are new, named entity recognition means finding the names of people, places and organizations in text. Run the sample, then replace the sentence with one of your own and read the tags. For experts, the labels use a begin and continue scheme, so two entities of the same kind in a row can be told apart. The card reports test scores of 91.3 F1, 90.7 precision and 91.9 recall, a little below the original BERT results. Subword pieces are sometimes tagged as entities, so you may need to merge or filter the output. ```flow Load the tokenizer and the model Build an ner pipeline Pass in a sentence of English text Each token receives an entity label Merge or filter the tags as needed ``` ```chart Title: Test scores from the card F1: 91.3 points Precision: 90.7 points Recall: 91.9 points ``` Good to know: The points below cover limits and costs. - Training data: English wire stories collected for CoNLL-2003, so text from other fields may be tagged less well. - License: MIT. - Cost to run: no API key or paid service is named. - Data it reaches: it reads the text you pass in. Downloading the model needs a connection to Hugging Face. - Not for: languages other than English, or texts that differ a lot from news. Subword tagging may need extra clean-up. ## On Hugging Face, 13 November 2026 - Task: token classification - Library: transformers - Parameters: 108M - Weights: about 433 MB in F32; about 54 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 1,038,928 - Likes: 750 - License: mit - Languages: en - Quantized versions: 9 - Fine-tunes: 41 - Tags: transformers, pytorch, onnx, safetensors, bert, token-classification, model-index ## Links - Model: https://huggingface.co/dslim/bert-base-NER - Files: https://huggingface.co/dslim/bert-base-NER/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download dslim/bert-base-NER ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [dslim/bert-base-NER on Hugging Face](https://huggingface.co/dslim/bert-base-NER) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | bert-base-NER (this page) | 1,038,928 | 750 | 108M | mit | 2024-10-08 | | [bge-small-en-v1.5](https://mindket.com/p/c288dc31-39c2-4e3e-85b8-0fec1d2b32b5) | 45,750,548 | 671 | 33.4M | mit | 2024-02-22 | | [bge-base-en-v1.5](https://mindket.com/p/daaf8719-1018-47cd-99d3-298e77acfad4) | 8,508,165 | 542 | 109M | mit | 2024-02-21 | | [AstrLink Guard](https://mindket.com/p/0a5aa215-212a-444e-b856-46e3303a3306) | 408 | 29 | — | apache-2.0 | 2026-10-23 | | [all-MiniLM-L6-v2](https://mindket.com/p/ee5187e2-9f20-47be-ae7e-3a6007276a39) | 174,740,184 | 6,104 | 22.7M | apache-2.0 | 2026-06-01 | | [BERT base model (uncased)](https://mindket.com/p/d5887db0-760c-4639-8457-d0da6a3a2699) | 31,346,528 | 3,071 | 110M | apache-2.0 | 2024-02-19 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · arnabkarsarkar · 2025-12-09](https://news.ycombinator.com/item?id=46206591): Show HN: Local Privacy Firewall-blocks PII and secrets before ChatGPT sees them: OP here. I built this because I recently caught myself almost pasting a block of logs containing AWS keys into Claude. The Problem: I need the reasoning capabilities of cloud models (GPT/Claude/Gemini), but I can't trust myself not to accidentally leak PII or secrets. The Solution: A Chrome… - [Hacker News · hmottestad · 2025-08-20](https://news.ycombinator.com/item?id=44965167): Gemma 3 270M re-implemented in pure PyTorch for local tinkering: Have you tried this one here by any chance? https://huggingface.co/dslim/bert-base-NER Just wondering if it’s worth testing and what it would be most useful for. - [Hacker News · hmottestad · 2025-07-02](https://news.ycombinator.com/item?id=44443934): Huawei releases an open weight model trained on Huawei Ascend GPUs: I was looking for one to use for named entity extraction and found this fine tune here: https://huggingface.co/dslim/bert-base-NER?utm\_source=chatgp... Its only 108 million params. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2024-10-08 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open dslim/bert-base-NER on Hugging Face](https://huggingface.co/dslim/bert-base-NER) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/20b1dab3-7f70-4c06-b584-f3c7be686fe2) - [Its page on Mindket](https://mindket.com/p/20b1dab3-7f70-4c06-b584-f3c7be686fe2)