Before you begin following the instructions for this Learning Path, make sure you have an Arm-based server running Ubuntu 22.04 LTS or later, with at least 8 cores, 16GB of RAM, and 30GB of disk storage.
ModelScope is an open-source platform designed to simplify the integration of AI models into applications. It offers a wide variety of pre-trained models for tasks such as image recognition, natural language processing, and audio analysis. With ModelScope, you can seamlessly integrate these models into your projects using just a few lines of code.
Key benefits of ModelScope:
Model Diversity - access a wide range of models for various tasks, including Automatic Speech Recognition (ASR), natural language processing (NLP), and computer vision.
Ease of Use - ModelScope provides a user-friendly interface and APIs that enable seamless model integration.
Community Support - benefit from a vibrant community of developers and researchers who actively contribute to and support ModelScope.
ModelScope fully supports PyTorch 1.8+ and other machine learning frameworks, enabling efficient deployment on Arm Neoverse CPUs. These CPUs leverage Arm’s performance and power efficiency advantages for optimized AI workloads.
Arm offers optimized software and tools, such as Kleidi, to accelerate AI model inference on Arm-based platforms. These enhancements make Arm Neoverse CPUs a powerful choice for deploying ModelScope models on edge devices and other resource-constrained environments.
You can learn more about Faster PyTorch Inference using Kleidi on Arm Neoverse on the Arm community website.
First, ensure your system is up to date and install the necessary tools and libraries:
sudo apt-get update -y
sudo apt-get install -y curl git wget python3 python3-pip python3-venv python-is-python3 ffmpeg
Create and activate a virtual environment:
python -m venv venv
source venv/bin/activate
In your active virtual environment, install ModelScope:
pip3 install modelscope
Install PyTorch and related dependencies:
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
pip3 install numpy packaging addict datasets simplejson sortedcontainers transformers ffmpeg
In this Learning Path you will execute models on the Arm Neoverse CPU, so you will only need to install the PyTorch CPU package.
You can now run an example to understand how to use ModelScope for understanding Chinese semantics.
Chinese writing differs fundamentally from English writing. In Chinese, the relationship between characters and their meanings is somewhat analogous to the distinction between words and phrases in English. Some Chinese characters, like English words, have clear meanings on their own, such as “人” (person), “山” (mountain), and “水” (water).
However, more often, Chinese characters need to be combined with other characters to express more complete meanings, much like phrases in English.
For example:
For computers to process Chinese sentences accurately, they must understand the rules governing Chinese characters, vocabulary, and grammar to interpret and express meaning correctly.
In this example, you will use a general-domain Chinese word segmentation model to break down Chinese sentences into individual words. This segmentation process helps computers analyze and understand Chinese text more effectively by identifying distinct words within a continuous string of characters.
Using a file editor of your choice, copy the code shown below into a file named segmentation.py:
from modelscope.pipelines import pipeline
word_segmentation = pipeline (
'word-segmentation',
model='damo/nlp_structbert_word-segmentation_chinese-base'
)
text = '一段新年祝福的文字跟所有人分享'
result = word_segmentation(text)
print(result)
This piece of code specifies a model and provides a Chinese sentence for the model to segment; “A New Year’s greeting message to share with everyone.”
Run the model inference on the sample text:
python3 segmentation.py
The output should look like this:
2025-01-28 00:30:29,692 - modelscope - WARNING - Model revision not specified, use revision: v1.0.3
Downloading Model to directory: /home/ubuntu/.cache/modelscope/hub/damo/nlp_structbert_word-segmentation_chinese-base
2025-01-28 00:30:32,828 - modelscope - WARNING - Model revision not specified, use revision: v1.0.3
2025-01-28 00:30:33,332 - modelscope - INFO - initiate model from /home/ubuntu/.cache/modelscope/hub/damo/nlp_structbert_word-segmentation_chinese-base
2025-01-28 00:30:33,333 - modelscope - INFO - initiate model from location /home/ubuntu/.cache/modelscope/hub/damo/nlp_structbert_word-segmentation_chinese-base.
2025-01-28 00:30:33,334 - modelscope - INFO - initialize model from /home/ubuntu/.cache/modelscope/hub/damo/nlp_structbert_word-segmentation_chinese-base
You are using a model of type bert to instantiate a model of type structbert. This is not supported for all configurations of models and can yield errors.
2025-01-28 00:30:35,522 - modelscope - WARNING - No preprocessor field found in cfg.
2025-01-28 00:30:35,522 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file.
2025-01-28 00:30:35,522 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'model_dir': '/home/ubuntu/.cache/modelscope/hub/damo/nlp_structbert_word-segmentation_chinese-base'}. trying to build by task and model information.
2025-01-28 00:30:35,527 - modelscope - INFO - cuda is not available, using cpu instead.
2025-01-28 00:30:35,529 - modelscope - WARNING - No preprocessor field found in cfg.
2025-01-28 00:30:35,529 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file.
2025-01-28 00:30:35,529 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'model_dir': '/home/ubuntu/.cache/modelscope/hub/damo/nlp_structbert_word-segmentation_chinese-base', 'sequence_length': 512}. trying to build by task and model information.
/home/ubuntu/venv/lib/python3.10/site-packages/transformers/modeling_utils.py:1044: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.
warnings.warn(
{'output': ['生成', '一', '段', '新年', '祝福', '的', '文字', '跟', '所有', '人', '分享']}
The segmentation model has correctly identified the following words:
一 (one): This is a numeral.
段 (piece): This is a measure word used for text.
新年 (New Year): This is a noun phrase meaning “New Year.”
祝福 (blessings): This is a noun meaning “blessings” or “good wishes.”
的 (of): This is a possessive particle.
文字 (text): This is a noun meaning “text” or “written words.”
跟 (with): This is a preposition meaning “with.”
所有 (all): This is a quantifier meaning “all.”
人 (people): This is a noun meaning “people.”
分享 (share): This is a verb meaning “to share.”
The segmentation model has successfully identified the word boundaries and separated the sentence into meaningful units, which is essential for further natural language processing tasks like machine translation or sentiment analysis.