Dccuchile
Models by this creator
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bert-base-spanish-wwm-uncased
51
bert-base-spanish-wwm-uncased is a Spanish language version of the BERT model, trained on a large Spanish corpus using the Whole Word Masking (WWM) technique. It is similar in size to the BERT-Base model, with 12 layers, 768 hidden dimensions, and 12 attention heads. The model was developed by the dccuchile team and outperforms the Multilingual BERT model on several Spanish language benchmarks such as part-of-speech tagging, named entity recognition, and text classification. Model inputs and outputs Inputs Tokenized text**: The model takes in tokenized Spanish text as input, which can be processed with the provided vocabulary and configuration files. Outputs Token embeddings**: The model outputs contextual token embeddings that can be used as features for downstream NLP tasks. Masked token predictions**: The model can be used to predict masked tokens in a sequence, enabling tasks like fill-in-the-blank or cloze-style exercises. Capabilities The bert-base-spanish-wwm-uncased model has demonstrated strong performance on a variety of Spanish language tasks, including part-of-speech tagging, named entity recognition, and text classification. For example, it achieved 98.97% accuracy on the Spanish POS tagging benchmark, outperforming the Multilingual BERT model. What can I use it for? The bert-base-spanish-wwm-uncased model can be used as a strong starting point for building Spanish language NLP applications. Some potential use cases include: Text classification**: Fine-tune the model on labeled Spanish text data to build classifiers for tasks like sentiment analysis, topic modeling, or intent detection. Named entity recognition**: Leverage the model's strong performance on the Spanish NER benchmark to extract entities like people, organizations, and locations from Spanish text. Question answering**: Fine-tune the model on a Spanish question-answering dataset to build a system that can answer questions based on given Spanish passages. Things to try One key insight about the bert-base-spanish-wwm-uncased model is its use of the Whole Word Masking technique during pretraining. This approach masks entire words rather than just individual tokens, which can lead to better contextual understanding and more accurate predictions, especially for morphologically rich languages like Spanish. Experimenting with different fine-tuning approaches that leverage this pretraining technique could yield interesting results.
Updated 5/28/2024
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bert-base-spanish-wwm-cased
50
bert-base-spanish-wwm-cased is a BERT model trained on a large Spanish corpus by the dccuchile team. It is similar in size to BERT-Base and was trained using the Whole Word Masking technique. This model outperforms the Multilingual BERT on several Spanish language benchmarks, including part-of-speech tagging, named entity recognition, and natural language inference. Model inputs and outputs bert-base-spanish-wwm-cased is a text-to-text transformer model that can be used for a variety of natural language processing tasks. The model takes in Spanish text as input and generates text-based outputs. Inputs Spanish text, such as sentences or paragraphs Outputs Text-based outputs for tasks like classification, named entity recognition, and language generation Capabilities The bert-base-spanish-wwm-cased model demonstrates strong performance on several Spanish language benchmarks, outperforming the Multilingual BERT model. It achieves high accuracy on part-of-speech tagging, named entity recognition, and natural language inference tasks. What can I use it for? The bert-base-spanish-wwm-cased model can be fine-tuned for a variety of Spanish language applications, such as text classification, named entity recognition, question answering, and language generation. It could be particularly useful for companies or developers working on Spanish-language products or services. Things to try Researchers and developers can explore fine-tuning bert-base-spanish-wwm-cased on specific Spanish language datasets or tasks to further improve its performance. The model's strong benchmark results suggest it could be a valuable starting point for building advanced Spanish language AI systems.
Updated 8/29/2024
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bert-base-spanish-wwm-cased
50
bert-base-spanish-wwm-cased is a BERT model trained on a large Spanish corpus by the dccuchile team. It is similar in size to BERT-Base and was trained using the Whole Word Masking technique. This model outperforms the Multilingual BERT on several Spanish language benchmarks, including part-of-speech tagging, named entity recognition, and natural language inference. Model inputs and outputs bert-base-spanish-wwm-cased is a text-to-text transformer model that can be used for a variety of natural language processing tasks. The model takes in Spanish text as input and generates text-based outputs. Inputs Spanish text, such as sentences or paragraphs Outputs Text-based outputs for tasks like classification, named entity recognition, and language generation Capabilities The bert-base-spanish-wwm-cased model demonstrates strong performance on several Spanish language benchmarks, outperforming the Multilingual BERT model. It achieves high accuracy on part-of-speech tagging, named entity recognition, and natural language inference tasks. What can I use it for? The bert-base-spanish-wwm-cased model can be fine-tuned for a variety of Spanish language applications, such as text classification, named entity recognition, question answering, and language generation. It could be particularly useful for companies or developers working on Spanish-language products or services. Things to try Researchers and developers can explore fine-tuning bert-base-spanish-wwm-cased on specific Spanish language datasets or tasks to further improve its performance. The model's strong benchmark results suggest it could be a valuable starting point for building advanced Spanish language AI systems.
Updated 8/29/2024