more docs update
@@ -853,7 +853,7 @@ nlu.load('tokenize').predict('Each word and symbol in a sentence will generate t
[Stemmer example](https://colab.research.google.com/drive/1gKTJJmffR9wz13Ms3pDy64jhUI8ZHZYu?usp=sharing)
```python
-nlu.load('stemm').predict('NLU can get you the stem of a word')
+nlu.load('stem').predict('NLU can get you the stem of a word')
```
{:.table-model-big.mb0}
@@ -976,7 +976,7 @@ nlu.load('match.datetime').predict('In the years 2000/01/01 to 2010/01/01 a lot
</div></div></div><div class="h3-box" markdown="1">
## Entity Chunking
-Checkout [see here](http://localhost:4000/docs/en/examples#part-of-speech--pos) for all possible POS labels or
+Checkout [see here](https://nlu.johnsnowlabs.com/docs/en/examples#part-of-speech--pos) for all possible POS labels or
Splits text into rows based on matched grammatical entities.
[Entity Chunking Example](https://colab.research.google.com/drive/1svpqtC3cY6JnRGeJngIPl2raqxdowpyi?usp=sharing)
@@ -1106,20 +1106,20 @@ You can translate between more than 192 Languages pairs with the [Marian Models]
You need to specify the language your data is in as `start_language` and the language you want to translate to as `target_language`.
The language references must be [ISO language codes](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes)
-`nlu.load('<start_language>.translate.<target_language>')`
+`nlu.load('xx.<start_language>.translate_to.<target_language>')`
**Translate Turkish to English:**
-`nlu.load('tr.translate_to.fr')`
+`nlu.load('xx.tr.translate_to.fr')`
**Translate English to French:**
-`nlu.load('en.translate_to.fr')`
+`nlu.load('xx.en.translate_to.fr')`
**Translate French to Hebrew:**
-`nlu.load('en.translate_to.fr')`
+`nlu.load('xx.en.translate_to.fr')`
```python
-translate_pipe = nlu.load('en.translate_to.de')
+translate_pipe = nlu.load('xx.en.translate_to.de')
df = translate_pipe.predict('Billy likes to go to the mall every sunday')
df
```
