• Sentence Classification (Sentiment Analysis): Indicate if the overall sentence is either positive or negative, i.e. binary classification task or logitic regression task.
• Token Classification (Named Entity Recognition, Part-of-Speech tagging): For each sub-entities (tokens) in the input, assign them a label, i.e. classification task.
• Question-Answering: Provided a tuple (question, context) the model should find the span of text in content answering the question.
• Mask-Filling: Suggests possible word(s) to fill the masked input with respect to the provided context.
• Summarization: Summarizes the input article to a shorter article.
• Translation: Translates the input from a language to another language.
• Feature Extraction: Maps the input to a higher, multi-dimensional space learned from the data.

Pipelines encapsulate the overall process of every NLP process:

1. Tokenization: Split the initial input into multiple sub-entities with ... properties (i.e. tokens).
2. Inference: Maps every tokens into a more meaningful representation.
3. Decoding: Use the above representation to generate and/or extract the final output for the underlying task.

Data set (https://www.kaggle.com/abhinavwalia95/entity-annotated-corpus)