bert-base-portuguese-cased
Maintainer: neuralmind
130
👁️
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The bert-base-portuguese-cased model, also known as "BERTimbau Base", is a pre-trained BERT model for the Brazilian Portuguese language developed by neuralmind. It achieves state-of-the-art performance on three key NLP tasks: Named Entity Recognition, Sentence Textual Similarity, and Recognizing Textual Entailment. This model is available in two sizes: Base and Large.
The BERT base model (cased) is a pre-trained model on English language data using a masked language modeling (MLM) objective. It makes a distinction between words like "english" and "English". The BERT base model (uncased) is another variant that does not differentiate between cases.
Model inputs and outputs
Inputs
- Text sequences in Brazilian Portuguese
Outputs
- Predictions on NLP tasks like Named Entity Recognition, Sentence Textual Similarity, and Recognizing Textual Entailment
Capabilities
The bert-base-portuguese-cased model excels at a variety of Portuguese language tasks, outperforming previous state-of-the-art models. For example, it can accurately identify named entities like locations, organizations, and people within Portuguese text. It can also assess the similarity between sentences and determine textual entailment - whether one sentence can be inferred from another.
What can I use it for?
The bert-base-portuguese-cased model is well-suited for building Portuguese language applications that require understanding and reasoning about text. This could include applications like:
- Information extraction
- Text classification
- Question answering
- Dialogue systems
Companies operating in Brazil or serving Portuguese-speaking audiences could leverage this model to add powerful language understanding capabilities to their products and services.
Things to try
One interesting aspect of the bert-base-portuguese-cased model is its ability to handle longer sequences of text. By incorporating the ALiBi position embedding technique, the model can effectively process input sequences up to 8,192 tokens in length. This makes it well-suited for applications that require understanding of long-form Portuguese content, such as research papers, technical documents, or literary works.
Another area to explore would be fine-tuning the model on domain-specific Portuguese data to further improve its performance on specialized tasks. The model's strong base capabilities provide a solid foundation for customization and adaptation to various business needs.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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The bert-base-cased model is a base-sized BERT model that has been pre-trained on a large corpus of English text using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive, meaning it can distinguish between words like "english" and "English". The BERT model learns a bidirectional representation of text by randomly masking 15% of the words in the input and then training the model to predict those masked words. This is different from traditional language models that process text sequentially. By learning to predict masked words in their full context, BERT can capture deeper semantic relationships in the text. Compared to similar models like bert-base-uncased, the bert-base-cased model preserves capitalization information, which can be useful for tasks like named entity recognition. The distilbert-base-uncased model is a compressed, faster version of BERT that was trained to mimic the behavior of the original BERT base model. The xlm-roberta-base model is a multilingual version of RoBERTa, capable of understanding 100 different languages. Model inputs and outputs Inputs Text**: The model takes raw text as input, which is tokenized and converted to token IDs that the model can process. Outputs Masked word predictions**: When used for masked language modeling, the model outputs probability distributions over the vocabulary for each masked token in the input. Sequence classifications**: When fine-tuned on downstream tasks, the model can output classifications for the entire input sequence, such as sentiment analysis or text categorization. Token classifications**: The model can also be fine-tuned to output classifications for individual tokens in the sequence, such as named entity recognition. Capabilities The bert-base-cased model is particularly well-suited for tasks that require understanding the full context of a piece of text, such as sentiment analysis, text classification, and question answering. Its bidirectional nature allows it to capture nuanced relationships between words that sequential models may miss. For example, the model can be used to classify whether a restaurant review is positive or negative, even if the review contains negation (e.g. "The food was not good"). By considering the entire context of the sentence, the model can understand that the reviewer is expressing a negative sentiment. What can I use it for? The bert-base-cased model is a versatile base model that can be fine-tuned for a wide variety of natural language processing tasks. Some potential use cases include: Text classification**: Classify documents, emails, or social media posts into categories like sentiment, topic, or intent. Named entity recognition**: Identify and extract entities like people, organizations, and locations from text. Question answering: Build a system that can answer questions by understanding the context of a given passage. Summarization**: Generate concise summaries of long-form text. Companies could leverage the model's capabilities to build intelligent chatbots, content moderation systems, or automated customer service applications. Things to try One interesting aspect of the bert-base-cased model is its ability to capture nuanced relationships between words, even across long-range dependencies. For example, try using the model to classify the sentiment of reviews that contain negation or sarcasm. You may find that it performs better than simpler models that only consider the individual words in isolation. Another interesting experiment would be to compare the performance of the bert-base-cased model to the bert-base-uncased model on tasks where capitalization is important, such as named entity recognition. The cased model may be better able to distinguish between proper nouns and common nouns, leading to improved performance.
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