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MGeo is a multi-modal multi-task geographic language model. We support 5 pipeline tasks and 1 pretrained model MGeo on maas. In the same time, we propose GeoGLUE, a geographic evaluation benchmark. MGeo can be finetuned on GeoGLUE tasks. Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11273012 * add prov city dist feature to gis encoder * finish mgeo fintune and pipeline * text classification add token type id * to_device support ModelOutput class * update token classification model lable mask logic
Introduction
ModelScope is a “Model-as-a-Service” (MaaS) platform that seeks to bring together most advanced machine learning models from the AI community, and to streamline the process of leveraging AI models in real applications. The core ModelScope library enables developers to perform inference, training and evaluation, through rich layers of API designs that facilitate a unified experience across state-of-the-art models from different AI domains.
The Python library offers the layered-APIs necessary for model contributors to integrate models from CV, NLP, Speech, Multi-Modality, as well as Scientific-computation, into the ModelScope ecosystem. Implementations for all these different models are encapsulated within the library in a way that allows easy and unified access. With such integration, model inference, finetuning, and evaluations can be done with only a few lines of codes. In the meantime, flexibilities are provided so that different components in the model applications can be customized as well, where necessary.
Apart from harboring implementations of various models, ModelScope library also enables the necessary interactions with ModelScope backend services, particularly with the Model-Hub and Dataset-Hub. Such interactions facilitate management of various entities (models and datasets) to be performed seamlessly under-the-hood, including entity lookup, version control, cache management, and many others.
Installation
Please refer to installation.
Get Started
You can refer to quick_start for quick start.
We also provide other documentations including:
- Introduction to tasks
- Use pipeline for model inference
- Finetune example
- Preprocessing of data
- Evaluation metrics
License
This project is licensed under the Apache License (Version 2.0).