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Distilbert Mlm 1000k
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Distilbert Mlm 1000k

Developed by vocab-transformers
DistilBERT is a lightweight distilled version of BERT, retaining 97% of BERT's performance while being 40% smaller and 60% faster.
Large Language Model
Transformers
#Knowledge-intensive NLP#Lightweight BERT#Multi-domain pre-training
Downloads 26
Release Time : 4/2/2022

Model Overview

DistilBERT is a Transformer-based pre-trained language model, compressed from the BERT model using knowledge distillation techniques. Suitable for natural language processing tasks such as text classification, question answering, and named entity recognition.

Model Features

Efficient and Lightweight
40% smaller in size and 60% faster in inference compared to the original BERT model, ideal for deployment in resource-constrained environments
High Performance
Retains 97% of BERT's performance, excelling in multiple NLP benchmark tests
Multi-task Adaptation
Supports fine-tuning for various downstream NLP tasks, including text classification, question answering, and named entity recognition

Model Capabilities

Text understanding
Text classification
Question answering systems
Named entity recognition
Semantic similarity calculation

Use Cases

Text Analysis
Sentiment Analysis
Analyze sentiment tendencies in social media or comments
Accuracy can exceed 90%
Spam Detection
Identify spam content in emails or messages
Information Extraction
Named Entity Recognition
Extract entities such as person names, locations, and organization names from text
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