返回到 Sentiment Analysis with Deep Learning using BERT
學生對 Coursera Project Network 提供的 Sentiment Analysis with Deep Learning using BERT 的評價和反饋
359 個評分
課程概述
In this 2-hour long project, you will learn how to analyze a dataset for sentiment analysis. You will learn how to read in a PyTorch BERT model, and adjust the architecture for multi-class classification. You will learn how to adjust an optimizer and scheduler for ideal training and performance. In fine-tuning this model, you will learn how to design a train and evaluate loop to monitor model performance as it trains, including saving and loading models. Finally, you will build a Sentiment Analysis model that leverages BERT's large-scale language knowledge.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
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FR
2020年10月11日
Clean, clear and helpful. Thanks a lot!\n\nWould also be nice to see the approaches to tune BERT for the particular task (e.g. custom tokenization, pre-processing of data, etc.)
GB
2020年7月27日
Thanks to Mr.Ari Anastassiou\n\nSentiment Analysis with Deep Learning using BERT! is been really a wonderful project .Enjoyed it
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76 - Sentiment Analysis with Deep Learning using BERT 的 76 個評論（共 76 個）
創建者 Raj P
•2021年1月2日
I didn't learn anything new here. This code and explanation is available on the web free of cost.
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