DistilBERT Tokenizer 256k - MLM 750k Open - source Model: Optimizing Word Vectors for Efficient Text Processing

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Distilbert Tokenizer 256k MLM 750k

Developed by vocab-transformers

DistilBERT model based on 256k word embeddings, optimized through MLM training

Large Language Model

Transformers

#Dynamic Word Embedding Updates #Large-scale Corpus Pretraining #Efficient MLM Fine-tuning

Downloads 16

Release Time : 4/7/2022

Model Overview

This model combines Word2Vec pretrained word embeddings with the DistilBERT architecture, trained on multiple corpora using MLM, suitable for natural language processing tasks

Model Features

Large-scale Word Embeddings

Initially loaded with 256k-dimensional Word2Vec embeddings, covering extensive vocabulary

Optimized MLM Training

Updated word embedding parameters during 750k training steps to enhance contextual representation

Multi-source Training Data

Trained on 100GB corpus including C4, MSMARCO, news, Wikipedia, and S2ORC

Lightweight Architecture

Utilizes DistilBERT architecture to reduce model size while maintaining performance

Model Capabilities

Text Representation Learning

Contextual Word Vector Generation

Language Model Fine-tuning

Transfer Learning for Downstream NLP Tasks

Use Cases

Information Retrieval

Search Relevance Modeling

Improving query-document matching using optimized word vectors

Text Classification

Document Classification

Feature extraction based on rich word embeddings

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Distilbert Tokenizer 256k MLM 750k

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 DistilBERT with 256k token embeddings

🚀 Quick Start