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131 lines
5.4 KiB
Markdown
131 lines
5.4 KiB
Markdown
<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>
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# spaCy examples
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For spaCy v3 we've converted many of the [v2 example
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scripts](https://github.com/explosion/spaCy/tree/v2.3.x/examples/) into
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end-to-end [spacy projects](https://spacy.io/usage/projects) workflows. The
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workflows include all the steps to go from data to packaged spaCy models.
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## 🪐 Pipeline component demos
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The simplest demos for training a single pipeline component are in the
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[`pipelines`](https://github.com/explosion/projects/blob/v3/pipelines) category
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including:
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- [`pipelines/ner_demo`](https://github.com/explosion/projects/blob/v3/pipelines/ner_demo):
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Train a named entity recognizer
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- [`pipelines/textcat_demo`](https://github.com/explosion/projects/blob/v3/pipelines/textcat_demo):
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Train a text classifier
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- [`pipelines/parser_intent_demo`](https://github.com/explosion/projects/blob/v3/pipelines/parser_intent_demo):
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Train a dependency parser for custom semantics
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## 🪐 Tutorials
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The [`tutorials`](https://github.com/explosion/projects/blob/v3/tutorials)
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category includes examples that work through specific NLP use cases end-to-end:
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- [`tutorials/textcat_goemotions`](https://github.com/explosion/projects/blob/v3/tutorials/textcat_goemotions):
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Train a text classifier to categorize emotions in Reddit posts
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- [`tutorials/nel_emerson`](https://github.com/explosion/projects/blob/v3/tutorials/nel_emerson):
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Use an entity linker to disambiguate mentions of the same name
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Check out the [projects documentation](https://spacy.io/usage/projects) and
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browse through the [available
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projects](https://github.com/explosion/projects/)!
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## 🚀 Get started with a demo project
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The
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[`pipelines/ner_demo`](https://github.com/explosion/projects/blob/v3/pipelines/ner_demo)
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project converts the spaCy v2
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[`train_ner.py`](https://github.com/explosion/spaCy/blob/v2.3.x/examples/training/train_ner.py)
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demo script into a spaCy v3 project.
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1. Clone the project:
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```bash
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python -m spacy project clone pipelines/ner_demo
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```
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2. Install requirements and download any data assets:
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```bash
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cd ner_demo
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python -m pip install -r requirements.txt
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python -m spacy project assets
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```
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3. Run the default workflow to convert, train and evaluate:
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```bash
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python -m spacy project run all
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```
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Sample output:
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```none
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ℹ Running workflow 'all'
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================================== convert ==================================
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Running command: /home/user/venv/bin/python scripts/convert.py en assets/train.json corpus/train.spacy
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Running command: /home/user/venv/bin/python scripts/convert.py en assets/dev.json corpus/dev.spacy
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=============================== create-config ===============================
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Running command: /home/user/venv/bin/python -m spacy init config --lang en --pipeline ner configs/config.cfg --force
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ℹ Generated config template specific for your use case
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- Language: en
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- Pipeline: ner
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- Optimize for: efficiency
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- Hardware: CPU
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- Transformer: None
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✔ Auto-filled config with all values
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✔ Saved config
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configs/config.cfg
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You can now add your data and train your pipeline:
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python -m spacy train config.cfg --paths.train ./train.spacy --paths.dev ./dev.spacy
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=================================== train ===================================
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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
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ℹ Using CPU
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=========================== Initializing pipeline ===========================
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[2021-03-11 19:34:59,101] [INFO] Set up nlp object from config
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[2021-03-11 19:34:59,109] [INFO] Pipeline: ['tok2vec', 'ner']
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[2021-03-11 19:34:59,113] [INFO] Created vocabulary
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[2021-03-11 19:34:59,113] [INFO] Finished initializing nlp object
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[2021-03-11 19:34:59,265] [INFO] Initialized pipeline components: ['tok2vec', 'ner']
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✔ Initialized pipeline
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============================= Training pipeline =============================
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ℹ Pipeline: ['tok2vec', 'ner']
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ℹ Initial learn rate: 0.001
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E # LOSS TOK2VEC LOSS NER ENTS_F ENTS_P ENTS_R SCORE
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--- ------ ------------ -------- ------ ------ ------ ------
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0 0 0.00 7.90 0.00 0.00 0.00 0.00
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10 10 0.11 71.07 0.00 0.00 0.00 0.00
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20 20 0.65 22.44 50.00 50.00 50.00 0.50
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30 30 0.22 6.38 80.00 66.67 100.00 0.80
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40 40 0.00 0.00 80.00 66.67 100.00 0.80
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50 50 0.00 0.00 80.00 66.67 100.00 0.80
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60 60 0.00 0.00 100.00 100.00 100.00 1.00
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70 70 0.00 0.00 100.00 100.00 100.00 1.00
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80 80 0.00 0.00 100.00 100.00 100.00 1.00
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90 90 0.00 0.00 100.00 100.00 100.00 1.00
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100 100 0.00 0.00 100.00 100.00 100.00 1.00
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✔ Saved pipeline to output directory
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training/model-last
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```
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4. Package the model:
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```bash
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python -m spacy project run package
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```
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5. Visualize the model's output with [Streamlit](https://streamlit.io):
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```bash
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python -m spacy project run visualize-model
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```
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