Ckip-joint
Rank:Average Model Cost: $0.0000
Number of Runs: 3,294
Models by this creator
bloom-3b-zh
bloom-3b-zh
BLOOM-zh Traditional Chinese-enhanced BLOOM language model Model Card Version 1.0 / 10.Apr.2023 BLOOM-zh is a joint collaboration between CKIP lab at Acedemia Sinica (link), MediaTek Research (ι£η΅, θΏη», link), and National Academy for Educational Research (link). This model is released for non-commerical research purposes only. Table of Contents Model Details Uses Training Data Risks and Limitations Recommendations Model Card Authors Model Details BLOOM-zh is a language model with enhanced Traditional Chinese capability. It is derived from BLOOMZ. BLOOM-zh is trained extendedly on large amount of Traditional Chinese text data. Basics Developed by: MediaTek Research Model Type: Transformer-based Language Model Version: 1.0.0 Languages: Multiple; see training data License: MEDIATEK RESEARCH License (link) and RAIL License v1.0 (link) Release Date Estimate: Monday, 10.April.2023 Send Questions to: info@mtkresearch.com Paper: https://arxiv.org/abs/2303.04715 Cite as: MediaTek Research: Traditional Chinese-enhanced BLOOM language model. International, February 2023. Organizations of contributors: MediaTek Research Academia Sinica National Academy for Educational Research Technical Specifications This section provides information for people who work on model development. For technical specifications, please refer to BLOOM. Environmental Impact For environmental impact, please refer to BLOOM. Uses This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model. It provides information for anyone considering using the model or who is affected by the model. For the uses of the model, please refer to BLOOM. Training Data This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning. We trained the 3B parameter model on a total of 13 Billion tokens of mostly high quality Traditional Chinese text. Details are provided in the paper. Risks and Limitations This section identifies foreseeable harms and misunderstandings. For risks and limitations, please refer to BLOOM. Factors This section lists some different aspects of BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior. The model is trained on Traditional Chinese. However, the pretrained weights capture more than 40 different languages. The model is trained on web crawled data, news articles, novels, knowledge sources (encyclopedia, education sector) and instructions. Recommendations This section provides information on warnings and potential mitigations. For recommendations, please refer to BLOOM. Model Card Authors Ordered roughly chronologically and by amount of time spent. Philipp Ennen, Po-Chun Hsu, Chan-Jan Hsu, Chang-Le Liu, Yen-Chen Wu, Yin-Hsiang Liao, Chin-Tung Lin, Chi-Ming Chung, Yi-Chang Chen, Da-Shan Shiu, Wei-Yun Ma
$-/run
1.0K
Huggingface