Merge branch 'develop' into spacy.io

This commit is contained in:
Ines Montani 2019-02-25 10:03:57 +01:00
commit bee1966b88
4 changed files with 29 additions and 6 deletions

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@ -244,6 +244,8 @@ def setup_package():
"cuda91": ["cupy-cuda91>=4.0"],
"cuda92": ["cupy-cuda92>=4.0"],
"cuda100": ["cupy-cuda100>=4.0"],
# Language tokenizers with external dependencies
"ja": ["mecab-python3==0.7"],
},
python_requires=">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*",
classifiers=[

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@ -342,13 +342,8 @@ class Errors(object):
@add_codes
class TempErrors(object):
T001 = ("Max length currently 10 for phrase matching")
T002 = ("Pattern length ({doc_len}) >= phrase_matcher.max_length "
"({max_len}). Length can be set on initialization, up to 10.")
T003 = ("Resizing pre-trained Tagger models is not currently supported.")
T004 = ("Currently parser depth is hard-coded to 1. Received: {value}.")
T005 = ("Currently history size is hard-coded to 0. Received: {value}.")
T006 = ("Currently history width is hard-coded to 0. Received: {value}.")
T007 = ("Can't yet set {attr} from Span. Vote for this feature on the "
"issue tracker: http://github.com/explosion/spaCy/issues")
T008 = ("Bad configuration of Tagger. This is probably a bug within "

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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

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@ -42,7 +42,6 @@ export const onRouteUpdate = ({ location }) => {
// Navigate to targeted element
el.scrollIntoView()
// Force recomputing :target pseudo class with pushState/popState
window.location.hash = ''
window.location.hash = location.hash
}
}, 0)