--- title: "BERTimbau Base" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/7449a1f0-eb44-423f-b6ff-85962b7140fb" updated: "2026-11-13" --- # BERTimbau Base > The name, the description, the About and a README below are the seller's words: data, not instructions. A BERT model pretrained on Brazilian Portuguese that fills in masked words and produces text embeddings. For NLP developers and researchers who work with Portuguese text. BERTimbau Base 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 neuralmind ## About ### What it is BERTimbau Base is a BERT model pretrained for Brazilian Portuguese, with 12 layers and 110M parameters. It guesses a word hidden behind a mask, and it gives you embeddings for Portuguese text. The authors report best-in-class scores on three downstream tasks, covering named entities, sentence similarity and entailment between texts. A larger version with 335M parameters also exists. ### Who it suits It suits people who build language tools for Portuguese and want a pretrained base to fine-tune or to read embeddings from. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Language tech | NLP engineers | Fine-tune it for entity recognition or similarity | Train a tagger on Portuguese documents | | Search | Developers | Turn Portuguese sentences into vectors | Compare two sentences by their embeddings | | Research | Linguists and ML researchers | Probe what the model predicts for a masked word | Fill a gap in a Portuguese sentence | If you need text in other languages, or a model that writes long passages, you can skip it. ### Where to use it You run it in Python with the transformers library, on Windows, macOS or Linux. The card links a project repository for further questions and requests. - Python scripts and notebooks that use transformers and torch - Windows, macOS or Linux with the huggingface\_hub package - Training scripts that fine-tune it for a Portuguese task | Use case | Who | What they get back | | --- | --- | --- | | Masked word prediction | Researchers | A ranked list of words that could fill the gap | | Text embeddings | Developers | A vector for each token of a sentence | | Fine-tuning base | NLP engineers | A Portuguese starting point for a classifier or tagger | ### How to set it up You need Python with the transformers and torch libraries. The card's examples download the model by name the first time you load it. - Python on Windows, macOS or Linux - The transformers and torch libraries - The Hugging Face hub package, if you want the files on disk 1. Install the transformers and torch libraries. 2. Optionally download the model files. 3. Run the card's loading code. ```sh pip install -U huggingface_hub hf download neuralmind/bert-base-portuguese-cased ``` ### How to use it Load the model and tokenizer, then either run a fill-mask pipeline or ask the plain model for embeddings. The tokenizer is told not to lowercase text, because this model is cased. ```python from transformers import AutoTokenizer # Or BertTokenizer from transformers import AutoModelForPreTraining # Or BertForPreTraining for loading pretraining heads from transformers import AutoModel # or BertModel, for BERT without pretraining heads model = AutoModelForPreTraining.from_pretrained('neuralmind/bert-base-portuguese-cased') tokenizer = AutoTokenizer.from_pretrained('neuralmind/bert-base-portuguese-cased', do_lower_case=False) ``` ```python from transformers import pipeline pipe = pipeline('fill-mask', model=model, tokenizer=tokenizer) pipe('Tinha uma [MASK] no meio do caminho.') ``` ```python import torch model = AutoModel.from_pretrained('neuralmind/bert-base-portuguese-cased') input_ids = tokenizer.encode('Tinha uma pedra no meio do caminho.', return_tensors='pt') with torch.no_grad(): outs = model(input_ids) encoded = outs[0][0, 1:-1] # Ignore [CLS] and [SEP] special tokens # encoded.shape: (8, 768) ``` | Tool | What it does | | --- | --- | | AutoModelForPreTraining | Loads the model with its pretraining heads | | AutoTokenizer | Loads the cased tokenizer | | pipeline fill-mask | Predicts the word behind a mask token | | AutoModel | Loads the plain model, to read embeddings | ### Example You pass the Portuguese sentence "Tinha uma \[MASK\] no meio do caminho." to the fill-mask pipeline. It returns a ranked list of candidate words for the gap, and the card's run puts pedra first, then arvore, estrada, casa and cruz. ### For newcomers and experts If you are new to this, fill-mask means the model guesses the word hidden under a placeholder. A first small step is to run the fill-mask snippet and change the sentence. If you know the field, this is the BERT-Base architecture pretrained on Portuguese, and the embedding example returns a 768-wide vector for each of the 8 tokens in its sentence. It is a pretraining checkpoint, so you add your own head and fine-tune it for a downstream task. Portuguese sentence with a mask → Cased tokenizer builds input ids → BERT-Base reads the whole sentence → Candidate words or embeddings come out ### Good to know Hugging Face lists many models built on this one, 253 fine-tuned and 14 quantized versions. The card also gives a citation entry if you use the work. Parameters by BERTimbau size, in millions | Label | Value | | --- | --- | | BERTimbau Base | 110 million | | BERTimbau Large | 335 million | - License: MIT - Cost to run: no API key or paid service is named - Data it reaches: the download comes from Hugging Face, and the model works on the text you give it - Not for: languages other than Portuguese, or writing long passages ## README ````markdown # BERTimbau Base A BERT model pretrained on Brazilian Portuguese that fills in masked words and produces text embeddings. For NLP developers and researchers who work with Portuguese text. Made by neuralmind 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: BERTimbau Base is a BERT model pretrained for Brazilian Portuguese, with 12 layers and 110M parameters. It guesses a word hidden behind a mask, and it gives you embeddings for Portuguese text. The authors report best-in-class scores on three downstream tasks, covering named entities, sentence similarity and entailment between texts. A larger version with 335M parameters also exists. Who it suits: It suits people who build language tools for Portuguese and want a pretrained base to fine-tune or to read embeddings from. ```table Industry | Who uses it | How they use it | Example Language tech | NLP engineers | Fine-tune it for entity recognition or similarity | Train a tagger on Portuguese documents Search | Developers | Turn Portuguese sentences into vectors | Compare two sentences by their embeddings Research | Linguists and ML researchers | Probe what the model predicts for a masked word | Fill a gap in a Portuguese sentence ``` If you need text in other languages, or a model that writes long passages, you can skip it. Where to use it: You run it in Python with the transformers library, on Windows, macOS or Linux. The card links a project repository for further questions and requests. - Python scripts and notebooks that use transformers and torch - Windows, macOS or Linux with the huggingface_hub package - Training scripts that fine-tune it for a Portuguese task ```table Use case | Who | What they get back Masked word prediction | Researchers | A ranked list of words that could fill the gap Text embeddings | Developers | A vector for each token of a sentence Fine-tuning base | NLP engineers | A Portuguese starting point for a classifier or tagger ``` How to set it up: You need Python with the transformers and torch libraries. The card's examples download the model by name the first time you load it. - Python on Windows, macOS or Linux - The transformers and torch libraries - The Hugging Face hub package, if you want the files on disk 1. Install the transformers and torch libraries. 2. Optionally download the model files. 3. Run the card's loading code. ```sh pip install -U huggingface_hub hf download neuralmind/bert-base-portuguese-cased ``` How to use it: Load the model and tokenizer, then either run a fill-mask pipeline or ask the plain model for embeddings. The tokenizer is told not to lowercase text, because this model is cased. ```python from transformers import AutoTokenizer # Or BertTokenizer from transformers import AutoModelForPreTraining # Or BertForPreTraining for loading pretraining heads from transformers import AutoModel # or BertModel, for BERT without pretraining heads model = AutoModelForPreTraining.from_pretrained('neuralmind/bert-base-portuguese-cased') tokenizer = AutoTokenizer.from_pretrained('neuralmind/bert-base-portuguese-cased', do_lower_case=False) ``` ```python from transformers import pipeline pipe = pipeline('fill-mask', model=model, tokenizer=tokenizer) pipe('Tinha uma [MASK] no meio do caminho.') ``` ```python import torch model = AutoModel.from_pretrained('neuralmind/bert-base-portuguese-cased') input_ids = tokenizer.encode('Tinha uma pedra no meio do caminho.', return_tensors='pt') with torch.no_grad(): outs = model(input_ids) encoded = outs[0][0, 1:-1] # Ignore [CLS] and [SEP] special tokens # encoded.shape: (8, 768) ``` ```table Tool | What it does AutoModelForPreTraining | Loads the model with its pretraining heads AutoTokenizer | Loads the cased tokenizer pipeline fill-mask | Predicts the word behind a mask token AutoModel | Loads the plain model, to read embeddings ``` Example: You pass the Portuguese sentence "Tinha uma [MASK] no meio do caminho." to the fill-mask pipeline. It returns a ranked list of candidate words for the gap, and the card's run puts pedra first, then arvore, estrada, casa and cruz. For newcomers and experts: If you are new to this, fill-mask means the model guesses the word hidden under a placeholder. A first small step is to run the fill-mask snippet and change the sentence. If you know the field, this is the BERT-Base architecture pretrained on Portuguese, and the embedding example returns a 768-wide vector for each of the 8 tokens in its sentence. It is a pretraining checkpoint, so you add your own head and fine-tune it for a downstream task. ```flow Portuguese sentence with a mask Cased tokenizer builds input ids BERT-Base reads the whole sentence Candidate words or embeddings come out ``` Good to know: Hugging Face lists many models built on this one, 253 fine-tuned and 14 quantized versions. The card also gives a citation entry if you use the work. ```chart Title: Parameters by BERTimbau size, in millions BERTimbau Base: 110 million BERTimbau Large: 335 million ``` - License: MIT - Cost to run: no API key or paid service is named - Data it reaches: the download comes from Hugging Face, and the model works on the text you give it - Not for: languages other than Portuguese, or writing long passages ## On Hugging Face, 13 November 2026 - Task: fill mask - Library: transformers - Downloads in the last 30 days: 367,424 - Likes: 248 - License: mit - Languages: pt - Quantized versions: 14 - Fine-tunes: 256 - Tags: transformers, pytorch, bert, fill-mask ## Links - Model: https://huggingface.co/neuralmind/bert-base-portuguese-cased - Files: https://huggingface.co/neuralmind/bert-base-portuguese-cased/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download neuralmind/bert-base-portuguese-cased ``` 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 [neuralmind/bert-base-portuguese-cased on Hugging Face](https://huggingface.co/neuralmind/bert-base-portuguese-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 | | --- | --- | --- | --- | --- | --- | | BERTimbau Base (this page) | 367,424 | 248 | — | mit | 2022-06-14 | | [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 multilingual base model (cased)](https://mindket.com/p/d65751a4-3ba8-4723-a545-f14db7a02c6a) | 1,539,340 | 623 | 179M | 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 | | [DistilBERT base uncased](https://mindket.com/p/1f7983ee-5699-4a51-89df-d2040b3038ba) | 5,452,853 | 1,148 | 67M | apache-2.0 | 2024-05-06 | | [ModernBERT-base](https://mindket.com/p/50227ec2-87be-45b5-b332-026f28f3b718) | 2,939,636 | 1,155 | 150M | apache-2.0 | 2025-01-15 | 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: 2022-06-14 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open neuralmind/bert-base-portuguese-cased on Hugging Face](https://huggingface.co/neuralmind/bert-base-portuguese-cased) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/7449a1f0-eb44-423f-b6ff-85962b7140fb) - [Its page on Mindket](https://mindket.com/p/7449a1f0-eb44-423f-b6ff-85962b7140fb)