wink-nlp - npm Package Compare versions

Comparing version 1.0.0 to 1.0.1

.nyc_output/processinfo/index.json

@@ -1,1 +0,1 @@
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CHANGELOG.md

@@ -0,1 +1,7 @@
+# [Operational update](https://github.com/winkjs/wink-nlp/releases/tag/1.0.1)
+## Version 1.0.1 August 24, 2020
+
+### ⚙️ Updates
+- Benchmarked on Node.js v12 & v14 also and updated the speed to minimum observed. 🏃‍♀️
+
 # [Announcing the stable version 1.0.0](https://github.com/winkjs/wink-nlp/releases/tag/1.0.0)
@@ -2,0 +8,0 @@ ## Version 1.0.0 August 21, 2020

package.json

 {
   "name": "wink-nlp",
-  "version": "1.0.0",
+  "version": "1.0.1",
   "description": "Developer friendly NLP ✨",
@@ -5,0 +5,0 @@ "keywords": [

README.md

@@ -8,3 +8,3 @@ # winkNLP
 
-winkNLP is a JavaScript library for Natural Language Processing (NLP). Designed specifically to make development of NLP solutions **easier** and **faster**, winkNLP is optimized for the right balance of performance and accuracy.  The package can handle large amount of raw text at speeds over **600,000 tokens/second**. And with a test coverage of ~100%, winkNLP is a tool for building production grade systems with confidence.
+winkNLP is a JavaScript library for Natural Language Processing (NLP). Designed specifically to make development of NLP solutions **easier** and **faster**, winkNLP is optimized for the right balance of performance and accuracy.  The package can handle large amount of raw text at speeds over **525,000 tokens/second**. And with a test coverage of ~100%, winkNLP is a tool for building production grade systems with confidence.
 
@@ -21,3 +21,3 @@ ## Features
 7. Word vector integration
-8. Comprehensive NLP pipeline covering tokenization, sentence boundary detection, negation handling, sentiment analysis, part-of-speech tagging, named entity extraction, custom entities detection and pattern matching
+8. Comprehensive NLP pipeline covering tokenization, sentence boundary detection, negation handling, sentiment analysis, part-of-speech (pos) tagging, named entity extraction, custom entities detection and pattern matching
 9. No external dependencies.
@@ -80,4 +80,6 @@
 ## Speed & Accuracy
-The [winkNLP](https://winkjs.org/wink-nlp/) processes raw text at **>600,000 tokens per second** with its default language model — [wink-eng-lite-model](https://github.com/winkjs/wink-eng-lite-model), when [benchmarked](https://github.com/bestiejs/benchmark.js) using "Ch 13 of Ulysses by James Joyce" on a 2.2 GHz Intel Core i7 machine with 16GB RAM. The benchmark covered the entire NLP pipeline — tokenization, sentence boundary detection, negation handling, sentiment analysis, part-of-speech tagging, and named entity extraction. This is way ahead of the prevailing speed benchmarks.
+The [winkNLP](https://winkjs.org/wink-nlp/) processes raw text at **~525,000 tokens per second** with its default language model — [wink-eng-lite-model](https://github.com/winkjs/wink-eng-lite-model), when [benchmarked](https://github.com/bestiejs/benchmark.js) using "Ch 13 of Ulysses by James Joyce" on a 2.2 GHz Intel Core i7 machine with 16GB RAM. The processing included the entire NLP pipeline — tokenization, sentence boundary detection, negation handling, sentiment analysis, part-of-speech tagging, and named entity extraction. This speed is way ahead of the prevailing speed benchmarks.
 
+The benchmark was conducted on [Node.js versions 14.8.0, 12.18.3 and 10.22.0](https://nodejs.org/en/about/releases/).
+
 It pos tags a subset of WSJ corpus with an accuracy of **~94.7%** — this includes *tokenization of raw text prior to pos tagging*. The current state-of-the-art is at ~97% accuracy but at lower speeds and is generally computed using gold standard pre-tokenized corpus.
@@ -87,3 +89,4 @@
 
-winkNLP delivers this performance with the minimal load on RAM. For example, it processes the entire [History of India Volume I](https://en.wikisource.org/wiki/History_of_India/Volume_1) with a peak memory requirement of under **80MB**. The book has around 350 pages which translates to over 125,000 tokens.
+## Memory Requirement
+Wink NLP delivers this performance with the minimal load on RAM. For example, it processes the entire [History of India Volume I](https://en.wikisource.org/wiki/History_of_India/Volume_1) with a total peak memory requirement of under **80MB**. The book has around 350 pages which translates to over 125,000 tokens.
 
@@ -90,0 +93,0 @@ ## Documentation

No alert changes

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