mirror of
https://github.com/explosion/spaCy.git
synced 2025-11-04 01:48:04 +03:00
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>
|
||
|
||
# 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
|
||
```
|