[简体中文](README.md) | English

**Table of contents**

- [1. Zero-shot Text Classification Application](#1)

- [2. Quick Start](#2)

- [2.1 Code Structure](#21)

- [2.2 Data Annotation](#22)

- [2.3 Finetuning](#23)

- [2.4 Evaluation](#24)

- [2.5 Inference](#25)

- [2.6 Deployment](#26)

- [2.7 Experiments](#27)

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This project provides an end-to-end application solution for universal text classification based on Universal Task Classification (UTC) finetuning and goes through the full lifecycle of **data labeling, model training and model deployment**. We hope this guide can help you apply Text Classification techniques with zero-shot ability in your own products or models.

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<img width="700" alt="UTC模型结构图" src="https://user-images.githubusercontent.com/25607475/212268807-66181bcb-d3f9-4086-9d4a-de4d1d0933c2.png">

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Text Classification refers to assigning a set of categories to given input text. Despite the advantages of tuning, applying text classification techniques in practice remains a challenge due to domain adaption and lack of labeled data, etc. This PaddleNLP Zero-shot Text Classification Guide builds on our UTC from the Unified Semantic Matching (USM) model series and provides an industrial-level solution that supports universal text classification tasks, including but not limited to **semantic analysis, semantic matching, intention recognition and event detection**, allowing you accomplish multiple tasks with a single model. Besides, our method brings good generation performance through multi-task pretraining.

**Highlights:**

- **Comprehensive Coverage**🎓: Covers various mainstream tasks of text classification, including but not limited to semantic analysis, semantic matching, intention recognition and event detection.

- **State-of-the-Art Performance**🏃: Strong performance from the UTC model, which ranks first on [ZeroCLUE](https://www.cluebenchmarks.com/zeroclue.html)/[FewCLUE](https://www.cluebenchmarks.com/fewclue.html) as of 01/11/2023.

- **Easy to use**⚡: Three lines of code to use our Taskflow for out-of-box Zero-shot Text Classification capability. One line of command to model training and model deployment.

- **Efficient Tuning**✊: Developers can easily get started with the data labeling and model training process without a background in Machine Learning.

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For quick start, you can directly use ```paddlenlp.Taskflow``` out-of-the-box, leveraging the zero-shot performance. For production use cases, we recommend labeling a small amount of data for model fine-tuning to further improve the performance.

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├── deploy/simple_serving/ # model deployment script

├── utils.py # data processing tools

├── run_train.py # model fine-tuning script

├── run_eval.py # model evaluation script

├── label_studio.py # data format conversion script

├── label_studio_text.md # data annotation instruction

└── README.md

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We recommend using [Label Studio](https://labelstud.io/) for data labeling. You can export labeled data in Label Studio and convert them into the required input format. Please refer to [Label Studio Data Labeling Guide](./label_studio_text_en.md) for more details.

Here we provide a pre-labeled example dataset `Medical Question Intent Classification Dataset`, which you can download with the following command. We will show how to use the data conversion script to generate training/validation/test set files for fine-tuning.

Download the medical question intent classification dataset:

wget https://bj.bcebos.com/paddlenlp/datasets/utc-medical.tar.gz

tar -xvf utc-medical.tar.gz

mv utc-medical data

rm utc-medical.tar.gz

Generate training/validation set files:

python label_studio.py \

--label_studio_file ./data/label_studio.json \

--save_dir ./data \

--splits 0.8 0.1 0.1 \

--options ./data/label.txt

For multi-task training, you can convert data with script seperately and move them to the same directory.

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Use the following command to fine-tune the model using `utc-large` as the pre-trained model, and save the fine-tuned model to `./checkpoint/model_best/`:

Single GPU:

python run_train.py \

--device gpu \

--logging_steps 10 \

--save_steps 100 \

--eval_steps 100 \

--seed 1000 \

--model_name_or_path utc-large \

--output_dir ./checkpoint/model_best \

--dataset_path ./data/ \

--max_seq_length 512 \

--per_device_train_batch_size 2 \

--per_device_eval_batch_size 2 \

--gradient_accumulation_steps 8 \

--num_train_epochs 20 \

--learning_rate 1e-5 \

--do_train \

--do_eval \

--do_export \

--export_model_dir ./checkpoint/model_best \

--overwrite_output_dir \

--disable_tqdm True \

--metric_for_best_model macro_f1 \

--load_best_model_at_end True \

--save_total_limit 1

Multiple GPUs:

python -u -m paddle.distributed.launch --gpus "0,1" run_train.py \

--device gpu \

--logging_steps 10 \

--save_steps 100 \

--eval_steps 100 \

--seed 1000 \

--model_name_or_path utc-large \

--output_dir ./checkpoint/model_best \

--dataset_path ./data/ \

--max_seq_length 512 \

--per_device_train_batch_size 2 \

--per_device_eval_batch_size 2 \

--gradient_accumulation_steps 8 \

--num_train_epochs 20 \

--learning_rate 1e-5 \

--do_train \

--do_eval \

--do_export \

--export_model_dir ./checkpoint/model_best \

--overwrite_output_dir \

--disable_tqdm True \

--metric_for_best_model macro_f1 \

--load_best_model_at_end True \

--save_total_limit 1

Parameters:

* `device`: Training device, one of 'cpu' and 'gpu' can be selected; the default is GPU training.

* `logging_steps`: The interval steps of log printing during training, the default is 10.

* `save_steps`: The number of interval steps to save the model checkpoint during training, the default is 100.

* `eval_steps`: The number of interval steps to save the model checkpoint during training, the default is 100.

* `seed`: global random seed, default is 42.

* `model_name_or_path`: The pre-trained model used for few shot training. Defaults to "utc-large".

* `output_dir`: Required, the model directory saved after model training or compression; the default is `None`.

* `dataset_path`: The directory to dataset; defaults to `./data`.

* `train_file`: Training file name; defaults to `train.txt`.

* `dev_file`: Development file name; defaults to `dev.txt`.

* `max_seq_len`: The maximum segmentation length of the text and label candidates. When the input exceeds the maximum length, the input text will be automatically segmented. The default is 512.

* `per_device_train_batch_size`: The batch size of each GPU core/CPU used for training, the default is 8.

* `per_device_eval_batch_size`: Batch size per GPU core/CPU for evaluation, default is 8.

* `num_train_epochs`: Training rounds, 100 can be selected when using early stopping method; the default is 10.

* `learning_rate`: The maximum learning rate for training, UTC recommends setting it to 1e-5; the default value is 3e-5.

* `do_train`: Whether to perform fine-tuning training, setting this parameter means to perform fine-tuning training, and it is not set by default.

* `do_eval`: Whether to evaluate, setting this parameter means to evaluate, the default is not set.

* `do_export`: Whether to export, setting this parameter means to export static graph, and it is not set by default.

* `export_model_dir`: Static map export address, the default is `./checkpoint/model_best`.

* `overwrite_output_dir`: If `True`, overwrite the contents of the output directory. If `output_dir` points to a checkpoint directory, use it to continue training.

* `disable_tqdm`: Whether to use tqdm progress bar.

* `metric_for_best_model`: Optimal model metric, UTC recommends setting it to `macro_f1`, the default is None.

* `load_best_model_at_end`: Whether to load the best model after training, usually used in conjunction with `metric_for_best_model`, the default is False.

* `save_total_limit`: If this parameter is set, the total number of checkpoints will be limited. Remove old checkpoints `output directory`, defaults to None.

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Model evaluation:

python evaluate.py \

--model_path ./checkpoint/model_best \

--test_path ./data/test.txt \

--per_device_eval_batch_size 2 \

--max_seq_len 512 \

--output_dir ./checkpoint_test

Parameters:

- `model_path`: The path of the model folder for evaluation, which must contain the model weight file `model_state.pdparams` and the configuration file `model_config.json`.

- `test_path`: The test set file for evaluation.

- `per_device_eval_batch_size`: Batch size, please adjust it according to the machine situation, the default is 8.

- `max_seq_len`: The maximum segmentation length of the text and label candidates. When the input exceeds the maximum length, the input text will be automatically segmented. The default is 512.

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You can use `paddlenlp.Taskflow` to load your custom model by specifying the path of the model weight file through `task_path`.

>>> from pprint import pprint

>>> from paddlenlp import Taskflow

>>> schema = ["病情诊断", "治疗方案", "病因分析", "指标解读", "就医建议", "疾病表述", "后果表述", "注意事项", "功效作用", "医疗费用", "其他"]

>>> my_cls = Taskflow("zero_shot_text_classification", schema=schema, task_path='./checkpoint/model_best', precision="fp16")

>>> pprint(my_cls("中性粒细胞比率偏低"))

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We provide the deployment solution on the foundation of PaddleNLP SimpleServing, where you can easily build your own deployment service with three-line code.

It supports FP16 (half-precision) and multiple process for inference acceleration.

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The results reported here are based on the development set of KUAKE-QIC.

| | Accuracy | Micro F1 | Macro F1 |

| :------: | :--------: | :--------: | :--------: |

| 0-shot | 28.69 | 87.03 | 60.90 |

| 5-shot | 64.75 | 93.34 | 80.33 |

| 10-shot | 65.88 | 93.76 | 81.34 |

| full-set | 81.81 | 96.65 | 89.87 |

where k-shot means that there are k annotated samples per label for training.

简体中文 | [English](label_studio_text_en.md)

在数据转换阶段，还需要提供标签候选信息，放在`./data/label.txt`文件中，每个标签占一行。例如在医疗意图分类中，标签候选为``["病情诊断", "治疗方案", "病因分析", "指标解读", "就医建议", "疾病表述", "后果表述", "注意事项", "功效作用", "医疗费用", "其他"]``，也可通过``options``参数直接进行配置。

- ``options``: 指定分类任务的类别标签。若输入类型为文件，则文件中每行一个标签。