roberta-large
Maintainer: FacebookAI
164
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| 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 roberta-large model is a large-sized Transformers model pre-trained by FacebookAI on a large corpus of English data using a masked language modeling (MLM) objective. It is a case-sensitive model, meaning it can distinguish between words like "english" and "English". The roberta-large model builds upon the BERT and XLM-RoBERTa architectures, providing enhanced performance on a variety of natural language processing tasks.
Model inputs and outputs
Inputs
- Raw text, which the model expects to be preprocessed into a sequence of tokens
Outputs
- Contextual embeddings for each token in the input sequence
- Predictions for masked tokens in the input
Capabilities
The roberta-large model excels at tasks that require understanding the overall meaning and context of a piece of text, such as sequence classification, token classification, and question answering. It can capture bidirectional relationships between words, allowing it to make more accurate predictions compared to models that process text sequentially.
What can I use it for?
You can use the roberta-large model to build a wide range of natural language processing applications, such as text classification, named entity recognition, and question-answering systems. The model's strong performance on a variety of benchmarks makes it a great starting point for fine-tuning on domain-specific datasets.
Things to try
One interesting aspect of the roberta-large model is its ability to handle case-sensitivity, which can be useful for tasks that require distinguishing between proper nouns and common nouns. You could experiment with using the model for tasks like named entity recognition or sentiment analysis, where case information can be an important signal.
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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