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What does downstream tasks terminology mean in NLP? I saw this terminology used in several articles but I can't understand the idea behind it.
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In those machine learning algorithms, you have so called "neurons" which are usually in form of a DAG. Downstream is everything after a certain neuron. You say neuron y is downstream of neuron x if and only if there is a directed path from x to y.
So, what is BERT? In simple words, BERT is an architecture that can be used for a lot of downstream tasks such as question answering, Classification, NER etc. One can assume a pre-trained BERT as a black box that provides us with H = 768 shaped vectors for each input token(word) in a sequence.
Downstream models are simply models that come after the model in question, in this case ResNet variants. Models for various topics within the computer vision domain often use a backbone to extract features from images, after which a downstream model is used to help to fit the model better to the task at hand.
In supervised learning, you can think of "downstream task" as the application of the language model. article classification: To tell whether the news is fake news? or Patent classification
Here are the main tasks fulfilled with the help of NLP. → Read how NLP social graph technique helps to assess patient databases can help clinical research organizations succeed with clinical trial analysis. Searching. NLP algorithms identify specific elements in the text.
There are two general approaches: abstractive and extractive summarization. In the first case, the NLP model creates an entirely new summary in terms of phrases and sentences used in the analyzed text. In the second case, the model extracts phrases and sentences from the existing text and groups them into a summary. Named-Entity Recognition.
Conclusion. Natural language processing can bring value to any business wanting to leverage unstructured data. The applications triggered by NLP models include sentiment analysis, summarization, machine translation, query answering and many more.
"Downstream tasks is what the field calls those supervised-learning tasks that utilize a pre-trained model or component". Derived from this blogpost: http://jalammar.github.io/illustrated-bert/
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