Merge pull request #73 from amaiya/develop

Develop
amaiya · Mar 4, 2020 · 3b91603 · 3b91603
2 parents 829bcd0 + d10ab05
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diff --git a/README.md b/README.md
@@ -5,7 +5,7 @@
 
 
 ### News and Announcements
-- **2020-03-02:**  
+- **2020-03-03:**  
   - ***ktrain*** **v0.10.x is released** and now includes [ready-to-use NER for English, Chinese, and Russian](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/develop/examples/text/shallownlp-examples.ipynb) with no training required. 
   - **Also in v0.10.x:**  Ability to train [community-uploaded Hugging Face transformer models](https://huggingface.co/models) like [SciBERT](https://arxiv.org/abs/1903.10676) and  [BioBERT](https://arxiv.org/abs/1901.08746):
 ```python

diff --git a/examples/README.md b/examples/README.md
@@ -106,7 +106,7 @@ The objective of the CoNLL2003 task is to classify sequences of words as belongi
 #### [shallownlp-examples.ipynb](https://github.com/amaiya/ktrain/tree/master/examples/text)
 - Shallow NLP is a submodule in *ktrain* that provides a small collection of miscellaneous text utiilities to analyze text on machines with no GPU and minimal computational resources.  Includes:
     - *text classification*: a non-neural version of NBSVM that can be trained in a single line of code
-	- *named-entity-recognition (NER)*:  out-of-the box, pretrained NER for English and Chinese that just works with no training
+	- *named-entity-recognition (NER)*:  out-of-the box, ready-to-use NER for English, Russian, and Chinese that just works with no training
 	- *multilingual text searches*:  searching text in multiple languages
 
 

diff --git a/examples/text/shallownlp-examples.ipynb b/examples/text/shallownlp-examples.ipynb
@@ -49,7 +49,7 @@
    "source": [
     "## SECTION 1: Ready-to-Use Named-Entity-Recognition\n",
     "\n",
-    "**ShallowNLP** includes pre-trained Named Entity Recognition (NER) for both English, Chinese, and Russian.\n",
+    "**ShallowNLP** includes pre-trained Named Entity Recognition (NER) for English, Chinese, and Russian.\n",
     "\n",
     "### English NER\n",
     "\n",