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Add table explaining training metrics [closes #2644]

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Ines Montani 2019-02-25 10:03:43 +01:00
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website/docs/usage/training.md
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@ -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