wink-nlp - npm Package Compare versions

Comparing version 0.4.0 to 1.0.0

.nyc_output/processinfo/index.json

@@ -1,1 +0,1 @@
-{"processes":{"71b34ff6-5fe8-4549-b0f2-a75730745589":{"parent":null,"children":[]}},"files":{"/Users/neilsbohr/dev/winkjs/wink-nlp/src/wink-nlp.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/dd-wrapper.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/constants.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/doc-v2.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/contained-entities.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/locate.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/get-parent-item.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/search.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/col-get-item.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/sel-get-item.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/col-each.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/sel-each.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/col-filter.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/sel-filter.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/itm-token-out.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/its.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/allowed.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/as.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/contained-markings.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/col-tokens-out.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/sel-tokens-out.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/itm-entity-out.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/col-entities-out.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/sel-entities-out.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/itm-sentence-out.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/col-sentences-out.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/itm-document-out.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/print-tokens.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/cache.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/tokenizer.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/recursive-tokenizer.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/compile-trex.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/tokens-mappers.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/examples-compiler.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/automaton.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/compose-patterns.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/helper.js":["71b34ff6-5fe8-4549-b0f2-a75730745589"]},"externalIds":{}}
+{"processes":{"76d7beb2-11b1-4446-bc2a-398d776404e9":{"parent":null,"children":[]}},"files":{"/Users/neilsbohr/dev/winkjs/wink-nlp/src/wink-nlp.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/dd-wrapper.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/constants.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/doc-v2.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/contained-entities.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/locate.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/get-parent-item.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/search.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/col-get-item.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/sel-get-item.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/col-each.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/sel-each.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/col-filter.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/sel-filter.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/itm-token-out.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/its.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/allowed.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/as.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/contained-markings.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/col-tokens-out.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/sel-tokens-out.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/itm-entity-out.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/col-entities-out.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/sel-entities-out.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/itm-sentence-out.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/col-sentences-out.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/itm-document-out.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/api/print-tokens.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/cache.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/tokenizer.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/recursive-tokenizer.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/compile-trex.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/tokens-mappers.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/examples-compiler.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/automaton.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/compose-patterns.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"],"/Users/neilsbohr/dev/winkjs/wink-nlp/src/helper.js":["76d7beb2-11b1-4446-bc2a-398d776404e9"]},"externalIds":{}}

CHANGELOG.md

@@ -0,1 +1,8 @@
+# [Announcing the stable version 1.0.0](https://github.com/winkjs/wink-nlp/releases/tag/1.0.0)
+## Version 1.0.0 August 21, 2020
+
+### ⚙️ Updates
+- Happy to release version 1.0.0 for you! 💫👏
+- You can optionally include custom entity detection while running speed benchmark. 😇
+
 # [Operational update](https://github.com/winkjs/wink-nlp/releases/tag/0.4.0)
@@ -2,0 +9,0 @@ ## Version 0.4.0 August 9, 2020

models/language-models.json

 [
-  { "name": "wink-eng-lite-model", "version": "0.3.0" }
+  { "name": "wink-eng-lite-model", "version": "1.0.0" }
 ]

package.json

 {
   "name": "wink-nlp",
-  "version": "0.4.0",
-  "description": "A new way of doing NLP ✨",
+  "version": "1.0.0",
+  "description": "Developer friendly NLP ✨",
   "keywords": [
@@ -15,3 +15,3 @@ "NLP",
     "named entity extraction",
-    "custome entity detection",
+    "custom entity detection",
     "word vectors",
@@ -18,0 +18,0 @@ "visualization",

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 **500,000 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 **600,000 tokens/second**. And with a test coverage of ~100%, winkNLP is a tool for building production grade systems with confidence.
 
@@ -78,3 +78,11 @@ ## Features
 
+## 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.
 
+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.
+
+Its general purpose sentiment analysis delivers a [f-score](https://en.wikipedia.org/wiki/F1_score) of **~84.5%**, when validated using Amazon Product Review [Sentiment Labelled Sentences Data Set](https://archive.ics.uci.edu/ml/machine-learning-databases/00331/) at [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php). The current benchmark accuracy for **specifically trained** models can range around 95%.
+
+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.
+
 ## Documentation
@@ -81,0 +89,0 @@ - [Concepts](https://winkjs.org/wink-nlp/getting-started.html) — everything you need to know to get started.

New alerts

License Policy Violation

License

This package is not allowed per your license policy. Review the package's license to ensure compliance.

Found 1 instance in 1 package

Fixed alerts

License Policy Violation

License

This package is not allowed per your license policy. Review the package's license to ensure compliance.

Found 1 instance in 1 package

No v1

Quality

Package is not semver >=1. This means it is not stable and does not support ^ ranges.

Found 1 instance in 1 package

Improved metrics

Total package byte prevSize

448115

increased by0.43%

Number of low quality alerts

0

decreased by-100%

Number of lines in readme file

111

increased by7.77%

No dependency changes