<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>
# spaCy examples
For spaCy v3 we've converted many of the [v2 example
scripts](https://github.com/explosion/spaCy/tree/v2.3.x/examples/) into
end-to-end [spacy projects](https://spacy.io/usage/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`](https://github.com/explosion/projects/blob/v3/pipelines) category
including:
- [`pipelines/ner_demo`](https://github.com/explosion/projects/blob/v3/pipelines/ner_demo):
Train a named entity recognizer
- [`pipelines/textcat_demo`](https://github.com/explosion/projects/blob/v3/pipelines/textcat_demo):
Train a text classifier
- [`pipelines/parser_intent_demo`](https://github.com/explosion/projects/blob/v3/pipelines/parser_intent_demo):
Train a dependency parser for custom semantics
## 🪐 Tutorials
The [`tutorials`](https://github.com/explosion/projects/blob/v3/tutorials)
category includes examples that work through specific NLP use cases end-to-end:
- [`tutorials/textcat_goemotions`](https://github.com/explosion/projects/blob/v3/tutorials/textcat_goemotions):
Train a text classifier to categorize emotions in Reddit posts
- [`tutorials/nel_emerson`](https://github.com/explosion/projects/blob/v3/tutorials/nel_emerson):
Use an entity linker to disambiguate mentions of the same name
Check out the [projects documentation](https://spacy.io/usage/projects) and
browse through the [available
projects](https://github.com/explosion/projects/)!
## 🚀 Get started with a demo project
The
[`pipelines/ner_demo`](https://github.com/explosion/projects/blob/v3/pipelines/ner_demo)
project converts the spaCy v2
[`train_ner.py`](https://github.com/explosion/spaCy/blob/v2.3.x/examples/training/train_ner.py)
demo script into a spaCy v3 project.
1. Clone the project:
```bash
python -m spacy project clone pipelines/ner_demo
```
2. Install requirements and download any data assets:
```bash
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:
```bash
python -m spacy project run all
```
Sample output:
```none
ℹ 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
ℹ 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
ℹ 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 =============================
ℹ Pipeline: ['tok2vec', 'ner']
ℹ 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:
```bash
python -m spacy project run package
```
5. Visualize the model's output with [Streamlit](https://streamlit.io):
```bash
python -m spacy project run visualize-model
```