sequence_classification

Sequence Classification

Token classification is a generic task encompasses any problem that can be formulated as “attributing a label to each token in a sentence,”.

Index

• Key Techniques
• Common Tasks
• Example Projects
• Evaluation Metrics
• Projects
  • MRPC - GLUE

Key Techniques

The key techniques we are going to use are Transformer-based models: Pre-trained models like BERT, RoBERTa, and their variants have become state-of-the-art for many sequence classification tasks.

Common Tasks

• Entailment: Determining whether a sentence entails, contradicts, or is neutral with respect to another sentence.
• Classification: Assigning a category or label to a sequence of tokens. For example for document classification, or spam detection.
• Sentiment Analysis: Determining the sentiment (positive, negative, neutral) of a sentence or document.

Example Projects

| NA

Evaluation Metrics

The common evaluation metrics for token classification tasks are:

• Precision: The proportion of true positive predictions among all positive predictions.
• Recall: The proportion of true positive predictions among all actual positive instances.
• F1 Score: The harmonic mean of precision and recall.
• Accuracy: The proportion of correct predictions among all predictions.

Projects

MRPC - GLUE

The Microsoft Research Paraphrase Corpus (MRPC) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent.

Fine-tune a model (BERT) on a Sequence Classification task, which will then be able to compute predictions.