distilroberta-base

Maintainer: distilbert

Total Score

121

Last updated 5/27/2024

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PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The distilroberta-base model is a distilled version of the RoBERTa-base model, developed by the Hugging Face team. It follows the same training procedure as the DistilBERT model, using a knowledge distillation approach to create a smaller and faster model while preserving over 95% of RoBERTa-base's performance. The model has 6 layers, 768 dimensions, and 12 heads, totaling 82 million parameters compared to 125 million for the full RoBERTa-base model.

Model inputs and outputs

The distilroberta-base model is a transformer-based language model that can be used for a variety of natural language processing tasks. It takes text as input and can be used for tasks like masked language modeling, where the model predicts missing words in a sentence, or for downstream tasks like sequence classification, token classification, or question answering.

Inputs

  • Text: The model takes text as input, which can be a single sentence, a paragraph, or even longer documents.

Outputs

  • Predicted tokens: For masked language modeling, the model outputs a probability distribution over the vocabulary for each masked token in the input.
  • Classification labels: When fine-tuned on a downstream task like sequence classification, the model outputs a label for the entire input sequence.
  • Answer spans: When fine-tuned on a question-answering task, the model outputs the start and end indices of the answer span within the input context.

Capabilities

The distilroberta-base model is a versatile language model that can be used for a variety of natural language processing tasks. It has been shown to perform well on tasks like sentiment analysis, natural language inference, and question answering, often with performance close to the full RoBERTa-base model while being more efficient and faster to run.

What can I use it for?

The distilroberta-base model is primarily intended to be fine-tuned on downstream tasks, as it is smaller and faster than the full RoBERTa-base model while maintaining similar performance. You can use it for tasks like:

  • Sequence classification: Fine-tune the model on a dataset like GLUE to perform tasks like sentiment analysis or natural language inference.
  • Token classification: Fine-tune the model on a dataset like CoNLL-2003 to perform named entity recognition.
  • Question answering: Fine-tune the model on a dataset like SQuAD to answer questions based on a given context.

Things to try

One interesting thing to try with the distilroberta-base model is to compare its performance to the full RoBERTa-base model on a range of tasks. Since the model is smaller and faster, it may be a good choice for deployment in resource-constrained environments or for applications that require quick inference times. Additionally, you can explore the model's limitations and biases by examining its behavior on prompts that might trigger harmful stereotypes or biases, as noted in the DistilBERT model card.

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