Update CI builds

README.md

@@ -1,13 +1,6 @@
-<<<<<<< HEAD
-# prose [![Build Status](https://travis-ci.org/jdkato/prose.svg?branch=master)](https://travis-ci.org/jdkato/prose) [![Build status](https://ci.appveyor.com/api/projects/status/24bepq85nnnk4scr/branch/master?svg=true)](https://ci.appveyor.com/project/jdkato/prose/branch/master) [![GoDoc](https://godoc.org/github.com/golang/gddo?status.svg)](https://godoc.org/github.com/jdkato/prose) [![Coverage Status](https://coveralls.io/repos/github/jdkato/prose/badge.svg?branch=master)](https://coveralls.io/github/jdkato/prose?branch=master) [![Go Report Card](https://goreportcard.com/badge/github.com/jdkato/prose)](https://goreportcard.com/report/github.com/jdkato/prose) [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/avelino/awesome-go#natural-language-processing)
-
-
-`prose` is Go library for text (primarily English at the moment) processing that supports tokenization, part-of-speech tagging, named-entity extraction, and more. The library's functionality is split into subpackages designed for modular use.
-=======
 # prose [![Build Status](https://travis-ci.org/jdkato/prose.svg?branch=master)](https://travis-ci.org/jdkato/prose) [![Build status](https://ci.appveyor.com/api/projects/status/24bepq85nnnk4scr?svg=true)](https://ci.appveyor.com/project/jdkato/prose) [![GoDoc](https://godoc.org/github.com/golang/gddo?status.svg)](https://godoc.org/gopkg.in/jdkato/prose.v2) [![Coverage Status](https://coveralls.io/repos/github/jdkato/prose/badge.svg?branch=v2)](https://coveralls.io/github/jdkato/prose?branch=v2) [![Go Report Card](https://goreportcard.com/badge/github.com/jdkato/prose)](https://goreportcard.com/report/github.com/jdkato/prose) [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/avelino/awesome-go#natural-language-processing)
 
 `prose` is a natural language processing library (English only, at the moment) in *pure Go*. It supports tokenization, segmentation, part-of-speech tagging, and named-entity extraction.
->>>>>>> v2
 
 You can can find a more detailed summary on the library's performance here: [Introducing `prose` v2.0.0: Bringing NLP *to Go*](https://medium.com/@errata.ai/introducing-prose-v2-0-0-bringing-nlp-to-go-a1f0c121e4a5).
 

appveyor.yml

@@ -1,6 +1,7 @@
 version: "{build}"
 os: Windows Server 2012 R2
 clone_folder: c:\GOPATH\src\github.com\jdkato\prose
+build: off
 environment:
   GOPATH: c:\GOPATH
 init:

extract.go

@@ -492,11 +492,7 @@ func encode(corpus featureSet) *binaryMaxentClassifier {
 		for _, fname := range featureOrder {
 			fval := entry.features[fname]
 			key := strings.Join([]string{fname, fval}, "-")
-			if _, ok := count[key]; ok {
-				count[key]++
-			} else {
-				count[key] = 1
-			}
+			count[key]++
 			entry := strings.Join([]string{fname, fval, label}, "-")
 			if _, found := mapping[entry]; !found {
 				mapping[entry] = len(mapping)

tag.go

@@ -313,11 +313,7 @@ func (m *averagedPerceptron) predict(features map[string]float64) string {
 			continue
 		}
 		for label, weight := range weights {
-			if _, ok := scores[label]; ok {
-				scores[label] += value * weight
-			} else {
-				scores[label] = value * weight
-			}
+			scores[label] += value * weight
 		}
 	}
 	return max(scores)
@@ -360,11 +356,7 @@ func featurize(i int, ctx []string, w, p1, p2 string) map[string]float64 {
 
 func add(args []string, features map[string]float64) map[string]float64 {
 	key := strings.Join(args, " ")
-	if _, ok := features[key]; ok {
-		features[key]++
-	} else {
-		features[key] = 1
-	}
+	features[key]++
 	return features
 }