distilbert-base-uncased-distilled-squad
Maintainer: distilbert
84
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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 distilbert-base-uncased-distilled-squad model is a smaller, faster version of the BERT base model that was trained using knowledge distillation. It was introduced in the blog post "Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT" and the paper "DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter". This DistilBERT model was fine-tuned on the SQuAD v1.1 dataset using a second step of knowledge distillation. It has 40% fewer parameters than the original BERT base model, runs 60% faster, while preserving over 95% of BERT's performance on the GLUE language understanding benchmark.
Model inputs and outputs
Inputs
- Question: A natural language question about a given context passage.
- Context: A passage of text that contains the answer to the question.
Outputs
- Answer: The span of text from the context that answers the question.
- Score: The confidence score of the predicted answer.
- Start/End Indices: The starting and ending character indices of the answer span within the context.
Capabilities
The distilbert-base-uncased-distilled-squad model is capable of answering questions about a given text passage, extracting the most relevant span of text to serve as the answer. For example, given the context:
Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a question answering dataset is the SQuAD dataset, which is entirely based on that task.
And the question "What is a good example of a question answering dataset?", the model would correctly predict the answer "SQuAD dataset".
What can I use it for?
This model can be leveraged for building question answering systems, where users can ask natural language questions about a given text and the model will extract the most relevant answer. This could be useful for building chatbots, search engines, or other information retrieval applications. The reduced size and increased speed of this DistilBERT model compared to the original BERT make it more practical for deploying in production environments with constrained compute resources.
Things to try
One interesting thing to try with this model is evaluating its performance on different types of questions and text domains beyond the SQuAD dataset it was fine-tuned on. The model may work well for factual, extractive questions, but its performance could degrade for more open-ended, complex questions that require deeper reasoning. Experimenting with the model's capabilities on a diverse set of question answering benchmarks would provide a more holistic understanding of its strengths and limitations.
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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