BERT base model (uncased)

BERT base model (uncased)

Fills in masked words and extracts text features for English. The uncased BERT base model from Google, meant to be fine-tuned for classification, tagging and question answering.

Made by google-bert 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: This is the base BERT model for English, pretrained on raw English text by predicting masked words and by judging whether two sentences follow each other. It is uncased, so english and English are treated alike. It has 110M parameters.

Who it suits: It suits developers and researchers who fine-tune a model for tasks that read whole sentences. People have published 7,169 fine-tuned versions of it.

Title: Who runs it
Industry | Who uses it | How they use it | Example
Software | NLP developers | Fine-tune it for sequence or token classification | A sentiment or topic classifier
Research | Language researchers | Use it as a standard English baseline | Compare a new method against BERT
Support | Teams handling English tickets | Train a classifier on its features | Route tickets by topic

If you want a model that writes text, you can skip it. The card points to GPT2-style models for that.

Where to use it:

• Python with the transformers library, on Windows, macOS or Linux
• Fine-tuning projects for classification, token classification and question answering
• Quick masked-word tests through the fill-mask pipeline

Title: Jobs it does
Use case | Who | What they get back
Masked word prediction | Developers | Ranked guesses for the hidden word
Text features | Researchers | Feature vectors you can feed to a classifier
Task fine-tuning | NLP teams | A base to train for classification or question answering

How to set it up: Python with the transformers library

1. Install transformers.
2. Optionally download the model files by hand.
3. Load the model by name in your code.

pip install -U huggingface_hub
hf download google-bert/bert-base-uncased

How to use it: Use the fill-mask pipeline for a quick test. The card says the raw model mainly serves as a starting point for fine-tuning on a task that looks at the whole sentence.

>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
>>> unmasker("Hello I'm a [MASK] model.")

Example: You give the pipeline the sentence "Hello I'm a [MASK] model." and it returns ranked guesses for the hidden word. In the card's output the top guesses are fashion, role, new and super, each shown in lowercase.

For newcomers and experts: If you are new, think of BERT as a model that learned English by filling in blanks. The first small thing to try is the fill-mask pipeline with a sentence that has one hidden word.

If you know the field, pretraining hides 15% of the words and adds next sentence prediction, which gives a bidirectional view of a sentence, unlike left-to-right models such as GPT. The trade-off is fit: it suits whole-sentence tasks after fine-tuning, not text generation.

Hide 15% of the words in a text
Run the whole sentence through BERT
Predict the hidden words
Reuse the features for a task

Good to know:

Title: Parameters across BERT releases
bert-base-uncased: 110 M
bert-large-uncased: 340 M
bert-base-chinese: 110 M
bert-base-multilingual-cased: 110 M

• Language: English only.
• Case: uncased, and the card says the uncased models also strip accent markers.
• Intended use: fine-tuning on whole-sentence tasks.
• Size: about 440 MB in F32.
• License: apache-2.0.
• Cost to run: no API key, account or paid service is named.
• Data it reaches: you download the model from Hugging Face, and the card names no other access.
• Not for: text generation, where the card suggests a model like GPT2.

On Hugging Face, 13 November 2026

• Task: fill mask
• Library: transformers
• Parameters: 110M
• Weights: about 440 MB in F32; about 55 MB at 4-bit (estimate)
• Formats: Safetensors
• Downloads in the last 30 days: 31,346,528
• Likes: 3,071
• License: apache-2.0
• Languages: en
• Quantized versions: 46
• Fine-tunes: 7,254
• Tags: transformers, pytorch, rust, coreml, onnx, safetensors, bert, fill-mask, exbert

Links

• Model: https://huggingface.co/google-bert/bert-base-uncased
• Files: https://huggingface.co/google-bert/bert-base-uncased/tree/main

Get the model

Download its files with Hugging Face's command line:

pip install -U huggingface_hub
hf download google-bert/bert-base-uncased

Then follow the usage in its model card. Nothing in this kit runs by itself.