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Training a Model with Custom Dataset

Generally speaking, Model = Data + Network + Training.

AdaSeq currently supports file-based mode, which means you need to clarify these arguments in a configuration file before starting training. And the configuration file is all you need to train a model.

This part of tutorial will introduce how to train a model with a custom dataset step by step in the sections to come.

1. Writing a Configuration File

Let's take resume.yaml as an example.

For detailed descriptions of the configuration arguments, please refer to this tutorial Learning about Configs.

1.1 Meta Settings

First, tell AdaSeq where to save all outputs during training.

experiment:
  exp_dir: experiments/
  exp_name: resume
  seed: 42

So the training logs, model checkpoints, prediction results and a copy of the configuration files will all be saved to ./experiments/resume/${datetime}/

1.2 Preparing Dataset

The dataset argument determines what the dataset look like (data format) and where to fetch the dataset (data source).

dataset:
  data_file:
    train: 'https://www.modelscope.cn/api/v1/datasets/damo/resume_ner/repo/files?Revision=master&FilePath=train.txt'
    valid: 'https://www.modelscope.cn/api/v1/datasets/damo/resume_ner/repo/files?Revision=master&FilePath=dev.txt'
    test: 'https://www.modelscope.cn/api/v1/datasets/damo/resume_ner/repo/files?Revision=master&FilePath=test.txt'
  data_type: conll

As the example snippet shows, AdaSeq will fetch the training, validation and testing dataset files from remote urls. The data is specified as conll format so that a corresponding script will be used to parse the data.

For more dataset loading approaches and supported data formats, please refer to this tutorial Customizing Dataset.

1.3 Assembling Model Architecture

This part specifies the task preprocessor data_collator model in the training.

The basic data flow is: dataset -> preprocessor -> data_collator -> model

preprocessor tells how AdaSeq processes a data sample. It needs a model_dir indicating the tokenizer to use and is used to turn a sentence into ids and masks.

data_collator tells how to collate data samples into batches.

model tells how a model is assembled where type indicates the basic architecture. A model usually consists of several replaceable components such as embeder, encoder, etc.

task: named-entity-recognition

preprocessor:
  type: sequence-labeling-preprocessor
  model_dir: sijunhe/nezha-cn-base
  max_length: 150

data_collator: SequenceLabelingDataCollatorWithPadding

model:
  type: sequence-labeling-model
  embedder:
    model_name_or_path: sijunhe/nezha-cn-base
  dropout: 0.1
  use_crf: true

1.4 Specifying Training Arguments

Last but not least, set some training and evaluation arguments in train and evaluation. Model performances can vary widely under different training settings.

This part is relatively easy to understand (I think). You can copy one and adjust the values to whatever you want.

train:
  max_epochs: 20
  dataloader:
    batch_size_per_gpu: 16
  optimizer:
    type: AdamW
    lr: 5.0e-5
    param_groups:
      - regex: crf
        lr: 5.0e-1
  lr_scheduler:
    type: LinearLR
    start_factor: 1.0
    end_factor: 0.0
    total_iters: 20

evaluation:
  dataloader:
    batch_size_per_gpu: 128
  metrics:
    - type: ner-metric
    - type: ner-dumper
      model_type: sequence_labeling
      dump_format: conll

2. Starting Training

Once you have a configuration file, it is easy to training a model. You can also use any of the configuration files in the examples. Just try it!

python scripts/train.py -c examples/bert_crf/configs/resume.yaml

We also provide advanced tutorials if you want to improve your training.

3. Fetching Evaluation Results and Predictions

During training, the process bar and the evaluation results will be logged to both the terminal and to a log file.

As we mentioned in 1.1 Meta Settings, all outputs will be saved to ./experiments/resume/${datetime}/. After training, there will be 5 files in the folder.

./experiments/resume/${datetime}/
├── best_model.pth
├── config.yaml
├── metrics.json
├── out.log
└── pred.txt

You can either collect the evaluation results in metrics.json or review all training logs in out.log. pred.txt will give predictions on the test dataset. You can analyze it to improve your model or submit it to some competition. best_model.pth can be used for further tuning or deployment.