Summary of Improving Sampling Methods For Fine-tuning Sentencebert in Text Streams, by Cristiano Mesquita Garcia et al.
Improving Sampling Methods for Fine-tuning SentenceBERT in Text Streams
by Cristiano Mesquita Garcia, Alessandro Lameiras Koerich, Alceu de Souza Britto Jr, Jean Paul Barddal
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the challenge of adapting pre-trained language models to concept drift in text stream mining settings. Concept drift occurs when data distributions change over time, affecting model performance. The study explores seven text sampling methods designed to selectively fine-tune language models and mitigate performance degradation. The authors precisely assess the impact of these methods on fine-tuning the SBERT model using four different loss functions. The evaluation focuses on Macro F1-score and elapsed time, employing two text stream datasets and an incremental SVM classifier. The findings indicate that Softmax loss and Batch All Triplets loss are effective for text stream classification, with larger sample sizes generally correlating with improved macro F1-scores. Notably, the proposed WordPieceToken ratio sampling method enhances performance with the identified loss functions, surpassing baseline results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to understand what people think about products and services online. There’s a lot of text data out there, but it changes over time, making it hard for machines to keep up. This study looks at how to make language models better at adapting to these changing trends. They tested seven different methods to fine-tune the model and found that some work much better than others. The best methods use a specific type of loss function and sample words in a special way. These methods can help improve how well the model does its job, making it more useful for understanding public opinion. |
Keywords
* Artificial intelligence * Classification * F1 score * Fine tuning * Loss function * Softmax
