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svlandeg 2020-08-03 14:55:06 +02:00
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website/docs/api/data-formats.md
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@ -95,6 +95,7 @@ annotations. You need to only specify those keys corresponding to the task you
want to train.
```python
### Flat dictionary
{
"text": string, # Raw text.
"words": List[string], # List of gold tokens.
@ -161,6 +162,7 @@ the previous section, but there are two main sections `token_annotation` and
[`Token` attributes](/api/token#attributes) such as "ORTH" and "TAG".
```python
### Hierarchical dictionary
{
"text": string, # Raw text.
"token_annotation": {

7
website/docs/api/example.md
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@ -41,7 +41,7 @@ both documents.
## Example.from_dict {#from_dict tag="classmethod"}
Construct an `Example` object from the `predicted` document and the reference
annotations provided as a dictionary. For more details on the required format,
annotations provided as a dictionary. For more details on the required format,
see the [training format documentation](/api/data-formats#dict-input).
> #### Example
@ -244,8 +244,9 @@ accuracy of predicted entities against the original gold-standard annotation.
## Example.to_dict {#to_dict tag="method"}
Return a dictionary representation of the reference annotation contained in this
`Example`.
Return a
[hierarchical dictionary representation](/api/data-formats#dict-hierarch) of the
reference annotation contained in this `Example`.
> #### Example
>