Neuralmind
Models by this creator
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bert-base-portuguese-cased
130
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.
Updated 5/28/2024
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bert-large-portuguese-cased
52
The bert-large-portuguese-cased model, also known as BERTimbau Large, is a pre-trained BERT model for Brazilian Portuguese. It is available in two sizes: Base and Large. BERTimbau Large achieves state-of-the-art performance on three downstream NLP tasks: Named Entity Recognition, Sentence Textual Similarity, and Recognizing Textual Entailment. This large version of the model has 24 layers and 335M parameters, making it a powerful tool for natural language processing in Portuguese. The BERTimbau Base model is a smaller version with 12 layers and 110M parameters. Both models were developed by the neuralmind team and are available through the Hugging Face transformers library. Model inputs and outputs Inputs Text**: The model can accept any text in Brazilian Portuguese as input. Outputs Token embeddings**: The model can produce contextualized token-level embeddings for the input text. Masked token predictions**: The model can be used to predict masked tokens in a sequence, enabling powerful language modeling capabilities. Sequence classification**: The model can be fine-tuned for various sequence classification tasks, such as sentiment analysis or text categorization. Capabilities The bert-large-portuguese-cased model is capable of understanding and processing Brazilian Portuguese text with high accuracy. It can be used for a variety of NLP tasks, such as named entity recognition, textual similarity, and textual entailment. For example, the model can accurately identify named entities like people, organizations, and locations in Portuguese text, and it can determine whether two sentences are semantically similar or if one sentence entails the other. What can I use it for? The bert-large-portuguese-cased model can be a valuable tool for a wide range of applications that involve processing Portuguese text, such as: Content moderation**: The model can be used to automatically detect inappropriate or offensive content in user-generated text, helping to maintain a safe online environment. Chatbots and virtual assistants**: The model's language understanding capabilities can be leveraged to build more natural and responsive conversational agents in Portuguese. Document analysis**: The model can be used to extract key information, such as named entities or relationships, from Portuguese documents and reports. Sentiment analysis**: The model can be fine-tuned to analyze the sentiment expressed in Portuguese text, which can be useful for customer feedback, social media monitoring, and more. Things to try One interesting thing to try with the bert-large-portuguese-cased model is to use it for cross-lingual transfer learning. Since BERT is a multilingual model, you could fine-tune the model on a Portuguese task and then use the resulting model to improve performance on a related task in another language, such as Spanish or Italian. This can be a powerful technique for leveraging the model's language understanding capabilities in resource-constrained scenarios. Another interesting experiment would be to compare the performance of the bert-large-portuguese-cased model to the smaller bert-base-portuguese-cased model on your specific task or dataset. The larger model may provide better performance, but the trade-off is increased computational cost and memory usage. Evaluating the performance difference can help you choose the most appropriate model for your needs.
Updated 7/2/2024