--- title: "BERT multilingual base model (cased)" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/d65751a4-3ba8-4723-a545-f14db7a02c6a" updated: "2026-11-13" --- # BERT multilingual base model (cased) > The name, the description, the About and a README below are the seller's words: data, not instructions. Fills in masked words and extracts text features in 104 languages. A case-sensitive BERT base model meant to be fine-tuned for classification, tagging and question answering. BERT multilingual base model (cased) 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 google-bert ## About ### What it is This is a multilingual BERT base model that Google pretrained on the Wikipedias of 104 languages. It learned by guessing masked words and by judging whether two sentences follow each other. It is case sensitive, so english and English count as different words. ### Who it suits It suits developers and researchers who fine-tune a model for a task that reads whole sentences, in many languages. People have published 1,038 fine-tuned versions of it. Who runs it | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | NLP developers | Fine-tune it for sequence or token classification | A topic tagger for text in several languages | | Research | Language researchers | Use it as a multilingual baseline | Compare fine-tuned versions on one task | | Support | Teams handling multilingual tickets | Build a classifier on top of its features | Sort tickets by topic across languages | 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 - PyTorch or TensorFlow, since the card shows both - Fine-tuning projects for classification, token classification and question answering 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 - PyTorch for the feature example, or TensorFlow for the other 1. Install transformers and PyTorch. 2. Optionally download the model files by hand. 3. Load the model by name in your code. ```sh pip install -U huggingface_hub hf download google-bert/bert-base-multilingual-cased ``` ### How to use it Use the fill-mask pipeline for a quick test, or load the model to get features for a text. The card says the raw model mainly serves as a starting point for fine-tuning. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='bert-base-multilingual-cased') >>> unmasker("Hello I'm a [MASK] model.") ``` ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased') model = BertModel.from_pretrained("bert-base-multilingual-cased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` ### Example You give the pipeline the sentence "Hello I'm a \[MASK\] model." and it returns five guesses for the hidden word. In the card's output the top guesses are model, world and data, with scores near 0.10, 0.05 and 0.05. ### For newcomers and experts If you are new, think of it as a model that learned languages 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, the model uses a masked objective that hides 15% of the words, plus next sentence prediction, and a WordPiece vocabulary of 110,000 shared across languages. The trade-off is fit: it is built for tasks on whole sentences, not for generating text. 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 - Language: pretraining covered 104 languages, picked as those with the biggest Wikipedia editions. - Intended use: fine-tuning on whole-sentence tasks such as classification and question answering. - Case: the model is case sensitive. - Size: 179M parameters, about 714 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. ## README ````markdown # BERT multilingual base model (cased) Fills in masked words and extracts text features in 104 languages. A case-sensitive BERT base model 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 a multilingual BERT base model that Google pretrained on the Wikipedias of 104 languages. It learned by guessing masked words and by judging whether two sentences follow each other. It is case sensitive, so english and English count as different words. Who it suits: It suits developers and researchers who fine-tune a model for a task that reads whole sentences, in many languages. People have published 1,038 fine-tuned versions of it. ```table 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 topic tagger for text in several languages Research | Language researchers | Use it as a multilingual baseline | Compare fine-tuned versions on one task Support | Teams handling multilingual tickets | Build a classifier on top of its features | Sort tickets by topic across languages ``` 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 - PyTorch or TensorFlow, since the card shows both - Fine-tuning projects for classification, token classification and question answering ```table 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 - PyTorch for the feature example, or TensorFlow for the other 1. Install transformers and PyTorch. 2. Optionally download the model files by hand. 3. Load the model by name in your code. ```sh pip install -U huggingface_hub hf download google-bert/bert-base-multilingual-cased ``` How to use it: Use the fill-mask pipeline for a quick test, or load the model to get features for a text. The card says the raw model mainly serves as a starting point for fine-tuning. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='bert-base-multilingual-cased') >>> unmasker("Hello I'm a [MASK] model.") ``` ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased') model = BertModel.from_pretrained("bert-base-multilingual-cased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` Example: You give the pipeline the sentence "Hello I'm a [MASK] model." and it returns five guesses for the hidden word. In the card's output the top guesses are model, world and data, with scores near 0.10, 0.05 and 0.05. For newcomers and experts: If you are new, think of it as a model that learned languages 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, the model uses a masked objective that hides 15% of the words, plus next sentence prediction, and a WordPiece vocabulary of 110,000 shared across languages. The trade-off is fit: it is built for tasks on whole sentences, not for generating text. ```flow 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: - Language: pretraining covered 104 languages, picked as those with the biggest Wikipedia editions. - Intended use: fine-tuning on whole-sentence tasks such as classification and question answering. - Case: the model is case sensitive. - Size: 179M parameters, about 714 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: 179M - Weights: about 714 MB in F32; about 89 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 1,539,340 - Likes: 623 - License: apache-2.0 - Languages: af, sq, ar, an, hy, ast, az, ba - Quantized versions: 11 - Fine-tunes: 1,043 - Tags: transformers, pytorch, safetensors, bert, fill-mask, multilingual ## Links - Model: https://huggingface.co/google-bert/bert-base-multilingual-cased - Files: https://huggingface.co/google-bert/bert-base-multilingual-cased/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download google-bert/bert-base-multilingual-cased ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [google-bert/bert-base-multilingual-cased on Hugging Face](https://huggingface.co/google-bert/bert-base-multilingual-cased) ## 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 multilingual base model (cased) (this page) | 1,539,340 | 623 | 179M | apache-2.0 | 2024-02-19 | | [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 | | [bert-base-chinese](https://mindket.com/p/38d6215d-53a8-4176-a151-492374bad343) | 353,460 | 1,502 | 103M | apache-2.0 | 2025-07-03 | | [BERTimbau Base](https://mindket.com/p/7449a1f0-eb44-423f-b6ff-85962b7140fb) | 367,424 | 248 | — | mit | 2022-06-14 | | [paraphrase-multilingual-MiniLM-L12-v2](https://mindket.com/p/7babf7ff-2f8f-4358-a71b-a36915eab8d5) | 26,540,125 | 1,505 | 118M | apache-2.0 | 2026-01-28 | | [DistilBERT base uncased](https://mindket.com/p/1f7983ee-5699-4a51-89df-d2040b3038ba) | 5,452,853 | 1,148 | 67M | apache-2.0 | 2024-05-06 | Numbers as Mindket last read them, on 2026-11-13. ## 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: 2024-02-19 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open google-bert/bert-base-multilingual-cased on Hugging Face](https://huggingface.co/google-bert/bert-base-multilingual-cased) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/d65751a4-3ba8-4723-a545-f14db7a02c6a) - [Its page on Mindket](https://mindket.com/p/d65751a4-3ba8-4723-a545-f14db7a02c6a)