Transformers Sentiment

DUUI implementation for selected Hugging-Face-based transformer sentiment tools.

Included Models

Name	Revision	Languages
cardiffnlp/twitter-roberta-base-sentiment	b636d90b2ed53d7ba6006cefd76f29cd354dd9da	EN
cardiffnlp/twitter-roberta-base-sentiment-latest	5916057ce88cf0a408a195082b6c06d3dce12552	EN
cardiffnlp/twitter-xlm-roberta-base-sentiment	f3e34b6c30bf27b6649f72eca85d0bbe79df1e55	AR, EN, FR, DE, HI, IT, SP, PT
clampert/multilingual-sentiment-covid19	eea3f8e26d2828dbf9f0f1d939dd868396ec863c	EN, FR, DE
cmarkea/distilcamembert-base-sentiment	b7804e295dc3cf2aa8ce8cff83f22e0bdd249558	FR
finiteautomata/bertweet-base-sentiment-analysis	cf6b0f60e84096e077c171fe3176093674370291	EN
j-hartmann/sentiment-roberta-large-english-3-classes	f995433eb6d79d26702ab9335bfde472a9933ee4	EN
LiYuan/amazon-review-sentiment-analysis	0aacda6423e43213da4e50a0f30cfcdb42a5c725	EN, DE, FR, ES, IT, NL
mdraw/german-news-sentiment-bert	7b4abebe1c3fcfbc62dc0435e480807a80c18210	DE
nlptown/bert-base-multilingual-uncased-sentiment	e06857fdb0325a7798a8fc361b417dfeec3a3b98	EN, DE, FR, ES, IT, NL
oliverguhr/german-sentiment-bert	c5c8dd0c5b966460dce1b7c5851bd90af1d2c6b6	DE
philschmid/distilbert-base-multilingual-cased-sentiment-2	83ff874f93aacbba79642abfe2a274a3c874232b	EN, DE, FR, ES, ZH, JA
siebert/sentiment-roberta-large-english	6eac71655a474ee4d6d0eee7fa532300c537856d	EN

How To Use

For using duui-transformers-sentiment as a DUUI image it is necessary to use the Docker Unified UIMA Interface (DUUI).

Start Docker container

docker run --rm -p 1000:9714 docker.texttechnologylab.org/textimager-duui-transformers-sentiment:latest

Find all available image tags here: https://docker.texttechnologylab.org/v2/textimager-duui-transformers-sentiment/tags/list

Run within DUUI

composer.add(
    new DUUIDockerDriver.Component("docker.texttechnologylab.org/textimager-duui-transformers-sentiment:latest")
        .withScale(iWorkers)
        .withImageFetching()
);

Parameters

Name	Description
model_name	Model to use, see table above
selection	Use text to process the full document text or any selectable UIMA type class name

Cite

If you want to use the DUUI image please quote this as follows:

Alexander Leonhardt, Giuseppe Abrami, Daniel Baumartz and Alexander Mehler. (2023). "Unlocking the Heterogeneous Landscape of Big Data NLP with DUUI." Findings of the Association for Computational Linguistics: EMNLP 2023, 385–399. [LINK] [PDF]

BibTeX

@inproceedings{Leonhardt:et:al:2023,
  title     = {Unlocking the Heterogeneous Landscape of Big Data {NLP} with {DUUI}},
  author    = {Leonhardt, Alexander and Abrami, Giuseppe and Baumartz, Daniel and Mehler, Alexander},
  editor    = {Bouamor, Houda and Pino, Juan and Bali, Kalika},
  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2023},
  year      = {2023},
  address   = {Singapore},
  publisher = {Association for Computational Linguistics},
  url       = {https://aclanthology.org/2023.findings-emnlp.29},
  pages     = {385--399},
  pdf       = {https://aclanthology.org/2023.findings-emnlp.29.pdf},
  abstract  = {Automatic analysis of large corpora is a complex task, especially
               in terms of time efficiency. This complexity is increased by the
               fact that flexible, extensible text analysis requires the continuous
               integration of ever new tools. Since there are no adequate frameworks
               for these purposes in the field of NLP, and especially in the
               context of UIMA, that are not outdated or unusable for security
               reasons, we present a new approach to address the latter task:
               Docker Unified UIMA Interface (DUUI), a scalable, flexible, lightweight,
               and feature-rich framework for automatic distributed analysis
               of text corpora that leverages Big Data experience and virtualization
               with Docker. We evaluate DUUI{'}s communication approach against
               a state-of-the-art approach and demonstrate its outstanding behavior
               in terms of time efficiency, enabling the analysis of big text
               data.}
}

@misc{Baumartz:2022,
  author         = {Baumartz, Daniel},
  title          = {Hugging-Face-based sentiment models as DUUI component},
  year           = {2022},
  howpublished   = {https://github.com/texttechnologylab/duui-uima/tree/main/duui-transformers-sentiment}
}