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Distilbert Mlm 250k
DistilBERT is a lightweight distilled version of BERT, retaining most of BERT's performance but with fewer parameters and faster inference speed.
Downloads 17
Release Time : 4/2/2022
Model Overview
DistilBERT is a lightweight language model based on BERT, trained using knowledge distillation techniques. It is suitable for various natural language processing tasks such as text classification, question answering, and named entity recognition.
Model Features
Lightweight and efficient
DistilBERT reduces the number of parameters through knowledge distillation while retaining most of BERT's performance, resulting in faster inference speed.
Multi-task support
Suitable for various natural language processing tasks, including text classification, question answering, and named entity recognition.
Pre-trained model
Pre-trained on multiple large datasets (C4, MSMARCO, Wikipedia, S2ORC, and news datasets).
Model Capabilities
Text classification
Question answering system
Named entity recognition
Text similarity calculation
Use Cases
Text classification
Sentiment analysis
Analyze the sentiment tendency of text to determine if it is positive, negative, or neutral.
Performs well on multiple sentiment analysis datasets.
Question answering system
Open-domain question answering
Answer various user questions, suitable for customer service bots and intelligent assistants.
Performs well on question answering datasets such as MSMARCO.
Named entity recognition
Entity extraction
Identify and classify named entities from text, such as person names, locations, and organization names.
Performs well on datasets such as CoNLL-2003.
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