f88b03ab72
The original backbone-head abstraction was not articheted well enough, the input and output parameters of backbone and head were in the form of **kwargs, which was implicit and might cause confustion. Therefore, the following adjustments were made:
原有backbone head抽象程度不够深，backbone 以及head输入输出参数为**kwargs，比较晦涩，同时很多功能无法支持扩展，因此做了如下调整：
1. Divide the basic model based on the structure to: encoder-only model, decoder-only model, single stage model, two stage model, etc., . Now, the encoder-only model was accomplished, while others are under design
2. Derive the structed task-models from the basic model structure above: a single structed task-model is mainly used to parse the backbone/head cfg, in order to apply the correct backbone or head components, some models might adjust the forward method from the basic model
3. Add the initialization parameters, input and output parameters to head class and backbone class, in order to reduce the understanding cost.
4. Remove the original nncrf class and chang it to backbone-head form with the lstm backbone and crf head.
5. Support `model = Model.from_pretrained('bert-based-fill-mask', task='text-classification')`, this method could correctly load the backbone even when the task is different from the original one in configuration.
6. Support loading the model through the transformer's automodel, in the case of quickly integrating the backbone model without coding
7. Unifiy the original task classes in each nlp model and the structed task-model classes, the structed task-model are largely reduce the redundant codes in the original task classed. Still under refactor
8. Support load model configuration from hf transformers config.json, if the model related configuration is missing. Only suppport NLP models
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).