docs(README): update speed & accuracy section

Author: sanjayaksaxena

README.md

@@ -77,9 +77,9 @@ console.log( doc.tokens().out( its.type, as.freqTable ) );
 ## 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 far lower speeds.
+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 accuracy for a **specifically trained** models can range around 95%.
+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.