spaCy/examples/README.md
2021-03-12 08:07:20 +01:00

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spaCy examples

For spaCy v3 we've converted many of the v2 example scripts into end-to-end spacy projects workflows. The workflows include all the steps to go from data to packaged spaCy models.

🪐 Pipeline component demos

The simplest demos for training a single pipeline component are in the pipelines category including:

🪐 Tutorials

The tutorials category includes examples that work through specific NLP use cases end-to-end:

Check out the projects documentation and browse through the available projects!

🚀 Get started with a demo project

The pipelines/ner_demo project converts the spaCy v2 train_ner.py demo script into a spaCy v3 project.

  1. Clone the project:

    python -m spacy project clone pipelines/ner_demo
    
  2. Install requirements and download any data assets:

    cd ner_demo
    python -m pip install -r requirements.txt
    python -m spacy project assets
    
  3. Run the default workflow to convert, train and evaluate:

    python -m spacy project run all
    

    Sample output:

     Running workflow 'all'
    
    ================================== convert ==================================
    Running command: /home/user/venv/bin/python scripts/convert.py en assets/train.json corpus/train.spacy
    Running command: /home/user/venv/bin/python scripts/convert.py en assets/dev.json corpus/dev.spacy
    
    =============================== create-config ===============================
    Running command: /home/user/venv/bin/python -m spacy init config --lang en --pipeline ner configs/config.cfg --force
     Generated config template specific for your use case
    - Language: en
    - Pipeline: ner
    - Optimize for: efficiency
    - Hardware: CPU
    - Transformer: None
    ✔ Auto-filled config with all values
    ✔ Saved config
    configs/config.cfg
    You can now add your data and train your pipeline:
    python -m spacy train config.cfg --paths.train ./train.spacy --paths.dev ./dev.spacy
    
    =================================== train ===================================
    Running command: /home/user/venv/bin/python -m spacy train configs/config.cfg --output training/ --paths.train corpus/train.spacy --paths.dev corpus/dev.spacy --training.eval_frequency 10 --training.max_steps 100 --gpu-id -1
     Using CPU
    
    =========================== Initializing pipeline ===========================
    [2021-03-11 19:34:59,101] [INFO] Set up nlp object from config
    [2021-03-11 19:34:59,109] [INFO] Pipeline: ['tok2vec', 'ner']
    [2021-03-11 19:34:59,113] [INFO] Created vocabulary
    [2021-03-11 19:34:59,113] [INFO] Finished initializing nlp object
    [2021-03-11 19:34:59,265] [INFO] Initialized pipeline components: ['tok2vec', 'ner']
    ✔ Initialized pipeline
    
    ============================= Training pipeline =============================
     Pipeline: ['tok2vec', 'ner']
     Initial learn rate: 0.001
    E    #       LOSS TOK2VEC  LOSS NER  ENTS_F  ENTS_P  ENTS_R  SCORE 
    ---  ------  ------------  --------  ------  ------  ------  ------
      0       0          0.00      7.90    0.00    0.00    0.00    0.00
     10      10          0.11     71.07    0.00    0.00    0.00    0.00
     20      20          0.65     22.44   50.00   50.00   50.00    0.50
     30      30          0.22      6.38   80.00   66.67  100.00    0.80
     40      40          0.00      0.00   80.00   66.67  100.00    0.80
     50      50          0.00      0.00   80.00   66.67  100.00    0.80
     60      60          0.00      0.00  100.00  100.00  100.00    1.00
     70      70          0.00      0.00  100.00  100.00  100.00    1.00
     80      80          0.00      0.00  100.00  100.00  100.00    1.00
     90      90          0.00      0.00  100.00  100.00  100.00    1.00
    100     100          0.00      0.00  100.00  100.00  100.00    1.00
    ✔ Saved pipeline to output directory
    training/model-last
    
  4. Package the model:

    python -m spacy project run package
    
  5. Visualize the model's output with Streamlit:

    python -m spacy project run visualize-model