Deep Nets for Poets

See paper here.

@article{church_yuan_guo_wu_yang_chen_2021,
title={Emerging trends: Deep nets for poets},
volume={27},
DOI={10.1017/S1351324921000231},
number={5},
journal={Natural Language Engineering},
publisher={Cambridge University Press},
author={Church, Kenneth Ward and Yuan, Xiaopeng and Guo, Sheng and Wu, Zewu and Yang, Yehua and Chen, Zeyu},
year={2021},
pages={631–645}}

Please feel free to send email: here. We would appreciate feedback on the examples posted here, as well as contributions with more examples and more solutions that would help users appreciate why they should be as excited about deep nets as we are.

Short Cuts

	PaddleHub	PaddleNLP	HuggingFaceHub	Fairseq	ESPnet
Image Classification	example		example
OCR	example
Sentiment	example		example
NER (named entity recognition)	example		example
QA (question answering)		example	example
MT (machine translation)	example		example	example
TTS (text to speech)	example		example		example
STT (speech to text)			example		example

Click here for examples of loading datasets from several sources, including PaddleNLP and HuggingFaceHub.

Background

Many years ago, well before the web, I gave some lectures called Unix for Poets at a Linguistics Summer School. At the time, we were really excited by what we could do with lexical resources and corpora (superhighway roadkill such as email, bboards, faxes).

• Question: What can we do with it all?
• Answer: It is better to do something simple than nothing at all

These lectures argued that poets (users such as linguists, lexicographers, translators) can do simple things themselves. Moreover, DIY (do it yourself) is more satisfying than begging for "help" from a system admin.

In Deep Nets for Poets, I want to make a similar argument, but for deep nets (as opposed to Unix). In particular, there are simple examples in subdirectories below this that show:

1. Image Classification: Pictures → Labels
2. OCR (Optical Character Recognition): Pictures → text (English and Chinese)
3. Sentiment Analysis: Text (English and Chinese) → positive or negative
4. NER: Named Entity Recognition: Text → named entities (substrings with labels such as person and location)
5. QA: Question Answering (SQuAD): Questions (text) → Answers (spans (substrings))
6. MT: Machine Translation: Text in source language → Text in target language
7. TTS: Text to Speech (also known as speech synthesis): Text → Audio
8. STT: Speech to Text (also known as speech recognition (ASR)): Audio → Text

Each subdirectory below should be self-explanatory. Since there is so much material here, I was concerned that users might feel overwhelmed. To address this concern, there is a separate subdirectory for each example. Each subdirectory can be studied independently. There are no dependencies between subdirectories.

The emphasis is on simplicity and generality. The code is short, easy to read and easy to understand. If one cares about speed or performance on a leaderboard, there are probably better alternatives elsewhere.

Warning, there may be some intermittent timeouts while loading big objects from far away. If you run into a timeout, please try again.

Most of our examples are based on a number of hubs such as PaddleHub and HuggingFaceHub. ML Commons is working on an alternative approach called mlcube.