Text-Classification-using-Paddle

🎨 Language

• English
• 中文

📝 Description

This code is used Paddle to do a text classification.

The text can be Chinese or English, but the model is monolingual. It means that you must know the language of the text input, and choose the right model to predict.

The monolingual models I used:

• for Chinese text: hfl/roberta-wwm-ext-large
• for English text: ernie-2.0-large-en

Of course, you can use the multilingual model to train, maybe the accuracy will not better than the monolingual model, I guess.

⚙ Environment

• It used 1 * NVIDIA Tesla V100 32G to train model(Recommended). Ensure that CUDA is installed.
• Of course you can use CPU to train model

🛠 Requirements

• Python 3.9
• paddlepaddle 2.1.3
  • If you need a CPU only version, please install the this version
  • Else if you need a GPU version, please install the right version that based on your GPU and CUDA. For example: paddlepaddle-gpu==2.1.3.post101
• paddlenlp 2.1.0

If you want to deploy models, you also need to install:

• fastapi 0.79
• uvicorn 0.18.2

📚 Files

• There are two folders:
  • 1-train: Only to train a model that can predict text
  • 2-deploy: Only to deploy the trained model on VPS, and set up API
• It used Jupyter Notebook to easily start. Of course you can convert .ipynb to .py
• The step is the index of the files
• For example, you will run the file started with 1-xxx.ipynb, and then run the file started with 2-xxx.ipynb
• The files in 1-train/checkpoint and 2-deploy/models folder are fake files! You must get these files after running ipynb

📖 Data

• There are only some example text in data folder

• You need to convert your data into a csv file, which split by \t

• example data:

  text_a	label
  Do you ever get a little bit tired of life	A
  Like you're not really happy but you don't wanna die	B
  ...	...
  Like you're hangin' by a thread but you gotta survive	B
  'Cause you gotta survive	C

• You have to confirm that there is no \t in text and labels. Important!!!

• You need to split the data into train(80%) and test(20%), of course the rate you can set by yourself

🎯 To Run

Maybe there are something you have to change. For example: path

• Step 1: run train.ipynb. After running, you can get the model in checkpoint folder
  • If your text language is Chinese, please run 1.1-train_Chinese.ipynb
  • Else if your text language is English, please run 1.2-train_English.ipynb
• Step 2(Optional): run 2-evaluate.ipynb. After running, you can get the classification reports
• Step 3(Optional): run 3-predict.ipynb. After running, you can get the prediction from your file
• Step 4(Optional): run 4-predict_only_one.ipynb. After running, you can predict only one text
• Step 5: run 5-to_static.ipynb. After running, you can get the infer model that can be deployed
• Step 6(Optional): run 6-infer.ipynb. A test to infer

📢 Deploy

After getting the infer model, you can deploy it by using FastAPI or other API framework.

Please copy your files into 2-deploy/models/English or 2-deploy/models/Chinese and make them in your model folder like:

• label_map.json
• model.pdiparams
• model.pdiparams.info
• model.pdmodel
• tokenizer_config.json
• vocab.txt

Run: python main.py

Visit: localhost:1234/docs to read docs

💡 Others

Docs about PaddlePaddle, PaddleNLP and FastAPI

• PaddlePaddle
• PaddleNLP
• FastAPI