Sparv-Superlim: A plugin for classifying text using the models trained on tasks in Superlim
Sparv-Superlim is a Sparv plugin for classifying text using the Superlim baseline models. Superlim is a multi-task benchmark for Swedish, which includes baseline models.
How to use?
Install Sparv-Superlim by injecting it into the Sparv Pipeline:
pipx inject sparv-pipeline git@github.com:spraakbanken/sparv-sbx-superlim.git
See the Sparv documentation for more details on how to install plugins.
Then make a config file and choose the relevant annotations:
metadata:
id: corpora
name:
eng: corpora
swe: korpora
language: swe
description:
eng: Swedish political manifestos with Superlim annotations
swe: Svenska valmanifest med Superlimannoteringar
import:
source_dir: source_small
importer: text_import:parse
export:
default:
- xml_export:pretty
- sbx_superlim:predictions
annotations:
- <token>
- <sentence>
- <sentence>:sbx_superlim.migration_stance
- <sentence>:sbx_superlim.nuclear_stance
sbx_superlim:
hf_model_path:
absabank-imm: 'sbx/bert-base-swedish-cased_absabank-imm'
argumentation: 'sbx/bert-base-swedish-cased-argumentation_sent'
hf_inference_args:
batch_size: 32
Plugin-specicific variables which start with hf are HuggingFace parameters. The most important one is the hf_model_path which tells which fine-tuned model to use for each task.
Full working examples can be found in the examples folder.
Available annotations
So far, Sparv-Superlim provides 10 different annotations. These are summarized in the table below:
| Superlim task | Sparv-Superlim Annotation | Annotation | Label | Segment |
|---|---|---|---|---|
| absabank-imm | migration_stance | Attitude towards immigration | float between 1-5 | sentence |
| argumentation- sentences | [topic]_stance | Stance to a given topic | pro, con or neutral | sentence |
| dalaj-ged | correct_swedish | Correct Swedish | correct or incorrect | sentence |
| swenli | previous_entailment | The logical relationship of two sentences | entailment, contradiction or neutral | sentence pair |
| sweparaphrase | similarity | Similarity between two sentences | float between 1-5 | sentence pair |
Wish to contribute?
Do you have new, innovative ways of incorporating models trained on Superlim into Sparv-Superlim? Make a feature request or even better a pull request!
How to cite?
Please cite the following technical report: Felix Morger. 2024. When Sparv met Superlim…A Sparv plugin for natural language understanding analysis of Swedish. Tech. rep. University of Gothenburg. You can also use the bibtex entry below.
@techreport{sparv-superlim,
title = {When {S}parv met {S}uperlim\ldots {A} {S}parv Plugin for Natural Language Understanding Analysis of {S}wedish},
author = {Morger, Felix},
url = {https://hdl.handle.net/2077/83664},
year = {2024},
publisher = {Språkbanken Text},
institution = {University of Gothenburg},
}
Metadata
Release files for sparv-sbx-superlim 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| sparv_sbx_superlim-0.1.0.tar.gz | 17.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| sparv_sbx_superlim-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 39.3 kB
Release files / sparv_sbx_superlim-0.1.0.tar.gz
| Download URL | sparv_sbx_superlim-0.1.0.tar.gz |
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