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Links to BERT base model (uncased) 본문
The model bert-base-uncased is used because it converts all text to lowercase before processing, ignoring case differences. This is particularly useful when case sensitivity is not important for the task, such as sentiment analysis, where "Happy" and "happy" should be treated the same. The "uncased" version is generally more efficient and performs well when the distinction between uppercase and lowercase letters does not add significant value to the model's performance.
https://huggingface.co/google-bert/bert-base-uncased
google-bert/bert-base-uncased · Hugging Face
BERT base model (uncased) Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is uncased: it does not make a difference between english and
huggingface.co
https://arxiv.org/abs/1810.04805
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unla
arxiv.org
Fine-tune BERT Model for Sentiment Analysis in Google Colab
In this blog, we will learn how to Fine-tune a Pre-trained BERT model for the Sentiment analysis task and execute it in Python. Start Reading Now!
www.analyticsvidhya.com
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| Links to Text Summarization with BART Model (0) | 2024.08.24 |
