Add table explaining training metrics [closes
@@ -39,6 +39,33 @@ mkdir models
python -m spacy train es models ancora-json/es_ancora-ud-train.json ancora-json/es_ancora-ud-dev.json
```
+#### Understanding the training output
+
+When you train a model using the [`spacy train`](/api/cli#train) command, you'll
+see a table showing metrics after each pass over the data. Here's what those
+metrics means:
+
+> #### Tokenization metrics
+>
+> Note that if the development data has raw text, some of the gold-standard
+> entities might not align to the predicted tokenization. These tokenization
+> errors are **excluded from the NER evaluation**. If your tokenization makes it
+> impossible for the model to predict 50% of your entities, your NER F-score
+> might still look good.
+
+| Name | Description |
+| ---------- | ------------------------------------------------------------------------------------------------- |
+| `Dep Loss` | Training loss for dependency parser. Should decrease, but usually not to 0. |
+| `NER Loss` | Training loss for named entity recognizer. Should decrease, but usually not to 0. |
+| `UAS` | Unlabeled attachment score for parser. The percentage of unlabeled correct arcs. Should increase. |
+| `NER P.` | NER precision on development data. Should increase. |
+| `NER R.` | NER recall on development data. Should increase. |
+| `NER F.` | NER F-score on development data. Should increase. |
+| `Tag %` | Fine-grained part-of-speech tag accuracy on development data. Should increase. |
+| `Token %` | Tokenization accuracy on development data. |
+| `CPU WPS` | Prediction speed on CPU in words per second, if available. Should stay stable. |
+| `GPU WPS` | Prediction speed on GPU in words per second, if available. Should stay stable. |
+
### Improving accuracy with transfer learning {#transfer-learning new="2.1"}
In most projects, you'll usually have a small amount of labelled data, and
