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pre-training
Pre-training is a concept often used in machine learning and natural language processing, involving training a model on a large dataset before fine-tuning it on a more specific task. This process helps the model to learn general features and patterns in the data, which can then be adapted to perform better on the specific task. Pre-training is particularly beneficial for tasks such as sentiment analysis, named entity recognition, and machine translation, and is widely used in models like BERT, GPT, and RoBERTa.
pre-training was discovered on June 25th 2020 and it currently has a search volume of 9.9K with a growth of +426%.
Growth
- Exploding
- Regular
- Peaked
Speed
- Exponential
- Constant
- Stationary
Seasonality
- High
- Medium
- Low
Volatility
- High
- Average
- Low
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