Preparing the Vuk'uzenzele and ZA-gov-multilingual South African multilingual corpora
Paper • 2303.03750 • Published
Text Generation
Transformers
PyTorch
Pedi
English
m2m_100
text2text-generation
m2m100
translation
africanlp
african
sepedi
northern-sotho
How to use from
SGLangUse Docker images
docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "dsfsi/nso-en-m2m100-gov" \
--host 0.0.0.0 \
--port 30000# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "dsfsi/nso-en-m2m100-gov",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
[nso-en] Northen Sotho [Sepedi] to English Translation Model based on M2M100 and The South African Gov-ZA multilingual corpus
Model created from Northen Sotho [Sepedi] to English aligned sentences from The South African Gov-ZA multilingual corpus
The data set contains cabinet statements from the South African government, maintained by the Government Communication and Information System (GCIS). Data was scraped from the governments website: https://www.gov.za/cabinet-statements
Authors
- Vukosi Marivate - @vukosi
- Matimba Shingange
- Richard Lastrucci
- Isheanesu Joseph Dzingirai
- Jenalea Rajab
BibTeX entry and citation info
@inproceedings{lastrucci-etal-2023-preparing,
title = "Preparing the Vuk{'}uzenzele and {ZA}-gov-multilingual {S}outh {A}frican multilingual corpora",
author = "Richard Lastrucci and Isheanesu Dzingirai and Jenalea Rajab and Andani Madodonga and Matimba Shingange and Daniel Njini and Vukosi Marivate",
booktitle = "Proceedings of the Fourth workshop on Resources for African Indigenous Languages (RAIL 2023)",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.rail-1.3",
pages = "18--25"
}
Paper - Preparing the Vuk'uzenzele and ZA-gov-multilingual South African multilingual corpora
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Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "dsfsi/nso-en-m2m100-gov" \ --host 0.0.0.0 \ --port 30000# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dsfsi/nso-en-m2m100-gov", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'