Add examples README

examples/README.md

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+<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
+   ```

examples/training/README.md

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+<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>
+
+# spaCy examples
+
+See [examples/README.md](../README.md)