bert-base-spanish-wwm-uncased
Maintainer: dccuchile
51
👀
| 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 |
Create account to get full access
Model overview
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.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
🤔
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 Invalid Date
🤔
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 Invalid Date
✨
bert-base-multilingual-cased
364
The bert-base-multilingual-cased model is a multilingual BERT model trained on the top 104 languages with the largest Wikipedia using a masked language modeling (MLM) objective. It was introduced in the paper "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" and first released in the google-research/bert repository. This cased model differs from the uncased version in that it maintains the distinction between uppercase and lowercase letters. BERT is a transformer-based model that was pretrained in a self-supervised manner on a large corpus of text data, without any human labeling. It was trained using two main objectives: masked language modeling, where the model must predict masked words in the input, and next sentence prediction, where the model predicts if two sentences were originally next to each other. This allows BERT to learn rich contextual representations of language that can be leveraged for a variety of downstream tasks. The bert-base-multilingual-cased model is part of a family of BERT models, including the bert-base-multilingual-uncased, bert-base-cased, and bert-base-uncased variants. These models differ in the language(s) they were trained on and whether they preserve case distinctions. Model inputs and outputs Inputs Text**: The model takes in raw text as input, which is tokenized and converted to token IDs that the model can process. Outputs Masked token predictions**: The model can be used to predict the masked tokens in an input sequence. Next sentence prediction**: The model can classify whether two input sentences were originally adjacent in the training data. Contextual embeddings**: The model can produce contextual embeddings for each token in the input, which can be used as features for downstream tasks. Capabilities The bert-base-multilingual-cased model is capable of understanding text in over 100 languages, making it useful for a wide range of multilingual applications. It can be used for tasks such as text classification, question answering, and named entity recognition, among others. One key capability of this model is its ability to capture the nuanced meanings of words by considering the full context of a sentence, rather than just looking at individual words. This allows it to better understand the semantics of language compared to more traditional approaches. What can I use it for? The bert-base-multilingual-cased model is primarily intended to be fine-tuned on downstream tasks, rather than used directly for tasks like text generation. You can find fine-tuned versions of this model on the Hugging Face Model Hub for a variety of tasks that may be of interest. Some potential use cases for this model include: Multilingual text classification**: Classifying documents or passages of text in multiple languages. Multilingual question answering**: Answering questions based on provided context, in multiple languages. Multilingual named entity recognition**: Identifying and extracting named entities (e.g., people, organizations, locations) in text across languages. Things to try One interesting thing to try with the bert-base-multilingual-cased model is to explore how its performance varies across different languages. Since it was trained on a diverse set of languages, it may exhibit varying levels of capability depending on the specific language and task. Another interesting experiment would be to compare the model's performance to the bert-base-multilingual-uncased variant, which does not preserve case distinctions. This could provide insights into how important case information is for certain multilingual language tasks. Overall, the bert-base-multilingual-cased model is a powerful multilingual language model that can be leveraged for a wide range of applications across many languages.
Updated Invalid Date
🛸
bert-base-uncased
1.6K
The bert-base-uncased model is a pre-trained BERT model from Google that was trained on a large corpus of English data using a masked language modeling (MLM) objective. It is the base version of the BERT model, which comes in both base and large variations. The uncased model does not differentiate between upper and lower case English text. The bert-base-uncased model demonstrates strong performance on a variety of NLP tasks, such as text classification, question answering, and named entity recognition. It can be fine-tuned on specific datasets for improved performance on downstream tasks. Similar models like distilbert-base-cased-distilled-squad have been trained by distilling knowledge from BERT to create a smaller, faster model. Model inputs and outputs Inputs Text Sequences**: The bert-base-uncased model takes in text sequences as input, typically in the form of tokenized and padded sequences of token IDs. Outputs Token-Level Logits**: The model outputs token-level logits, which can be used for tasks like masked language modeling or sequence classification. Sequence-Level Representations**: The model also produces sequence-level representations that can be used as features for downstream tasks. Capabilities The bert-base-uncased model is a powerful language understanding model that can be used for a wide variety of NLP tasks. It has demonstrated strong performance on benchmarks like GLUE, and can be effectively fine-tuned for specific applications. For example, the model can be used for text classification, named entity recognition, question answering, and more. What can I use it for? The bert-base-uncased model can be used as a starting point for building NLP applications in a variety of domains. For example, you could fine-tune the model on a dataset of product reviews to build a sentiment analysis system. Or you could use the model to power a question answering system for an FAQ website. The model's versatility makes it a valuable tool for many NLP use cases. Things to try One interesting thing to try with the bert-base-uncased model is to explore how its performance varies across different types of text. For example, you could fine-tune the model on specialized domains like legal or medical text and see how it compares to its general performance on benchmarks. Additionally, you could experiment with different fine-tuning strategies, such as using different learning rates or regularization techniques, to further optimize the model's performance for your specific use case.
Updated Invalid Date