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+7
-0
@@ -0,1 +1,8 @@ | ||
| # [Enabling loading of BM25Vectorizer model](https://github.com/winkjs/wink-nlp/releases/tag/1.10.0) | ||
| ## Version 1.10.0 November 18, 2021 | ||
| ### ✨ Features | ||
| - Easily load BM25Vectorizer's model using newly introduced `.loadModel()` api. 🎉 | ||
| # [Enhancing Typescript support](https://github.com/winkjs/wink-nlp/releases/tag/1.9.0) | ||
@@ -2,0 +9,0 @@ ## Version 1.9.0 November 06, 2021 |
+1
-1
| { | ||
| "name": "wink-nlp", | ||
| "version": "1.9.0", | ||
| "version": "1.10.0", | ||
| "description": "Developer friendly Natural Language Processing ✨", | ||
@@ -5,0 +5,0 @@ "keywords": [ |
+2
-0
@@ -121,2 +121,4 @@ # winkNLP | ||
| The [winkNLP](https://winkjs.org/wink-nlp/) delivers similar performance on browsers; its performance on a specific machine/browser combination can be measured using the Observable notebook — [How to measure winkNLP's speed on browsers?](https://observablehq.com/@winkjs/how-to-measure-winknlps-speed-on-browsers?collection=@winkjs/winknlp-recipes). | ||
| 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. | ||
@@ -123,0 +125,0 @@ |
+9
-2
@@ -208,6 +208,13 @@ // wink-nlp | ||
| its.modelJSON = function ( tf, idf ) { | ||
| return JSON.stringify( { tf: tf, idf: idf } ); | ||
| its.modelJSON = function ( tf, idf, terms, docId, sumOfAllDLs ) { | ||
| return JSON.stringify( { | ||
| uid: 'WinkNLP-BM25Vectorizer-Model/1.0.0', | ||
| tf: tf, | ||
| idf: idf, | ||
| terms: terms, | ||
| docId: docId, | ||
| sumOfAllDLs: sumOfAllDLs | ||
| } ); | ||
| }; // model() | ||
| module.exports = its; |
+3
-2
@@ -309,3 +309,3 @@ // Minimum TypeScript Version: 4.0 | ||
| export type Norm = "l2" | "NONE"; | ||
| export type Norm = "l1" | "l2" | "none"; | ||
@@ -321,6 +321,7 @@ export interface BM25VectorizerConfig { | ||
| learn(tokens: Tokens): void; | ||
| out<T>(f: ItsFunction<T>): T; | ||
| doc(n: number): Document; | ||
| out<T>(f: ItsFunction<T>): T; | ||
| vectorOf(tokens: Tokens): number[]; | ||
| config(): BM25VectorizerConfig; | ||
| loadModel(json: string): void; | ||
| } | ||
@@ -327,0 +328,0 @@ |
@@ -98,3 +98,3 @@ // wink-nlp | ||
| // Setup precision. | ||
| const precision = getValidCfgNum( cfg.precision, 9, 1, 18 ); | ||
| const precision = getValidCfgNum( cfg.precision, 6, 1, 12 ); | ||
| // Setup norm. | ||
@@ -138,2 +138,3 @@ const norm = ( | ||
| if ( weightsComputed ) return; | ||
| if ( docId === 0 ) throw Error( 'wink-nlp: this operation doesn\'t make sense without any learning; use learn() API first.' ); | ||
| // Set the average document length used for normalization. | ||
@@ -223,4 +224,7 @@ const avgDL = sumOfAllDLs / docId; | ||
| computeWeights(); | ||
| if ( allowed.its4BM25.has( f ) ) return f( tf, idf, terms ); | ||
| return its.docBOWArray( tf, idf, terms ); | ||
| // Pass `docId` & `sumOfAllDLs` in additionn to `tf`, `idf` & `terms`; this | ||
| // is needed while saving the model JSON. | ||
| if ( allowed.its4BM25.has( f ) ) return f( tf, idf, terms, docId, sumOfAllDLs ); | ||
| // In case of innvalid `f`, fall back to the default method — `docBOWArray`. | ||
| return its.docBOWArray( tf, idf, terms, docId, sumOfAllDLs ); | ||
| }; // out() | ||
@@ -274,13 +278,2 @@ | ||
| // ## length | ||
| /** | ||
| * | ||
| * Returns the number of unique tokens in the entire corpus. | ||
| * | ||
| * @return {number} the number of unique tokens in the corpus. | ||
| */ | ||
| methods.length = function () { | ||
| return Object.keys( idf ).length; | ||
| }; // length() | ||
| // ## vectorOf | ||
@@ -316,3 +309,3 @@ /** | ||
| // `thisNorm || 1` ensures that there is no attempt to divide by zero! | ||
| return arr.map( ( v ) => +( v / ( thisNorm || 1 ) ).toFixed( 9 ) ); | ||
| return arr.map( ( v ) => +( v / ( thisNorm || 1 ) ).toFixed( precision ) ); | ||
| }; // vectorOf() | ||
@@ -322,2 +315,44 @@ | ||
| // ## loadModel | ||
| /** | ||
| * Loads the input model JSON into the BM25's respective data structure. Throws | ||
| * error if invalid JSON or model is passed. Sets `weightsComputed` to true to | ||
| * prevent further learning. | ||
| * @param {string} json Input model's JSON string. | ||
| * @return {void} Nothing! | ||
| */ | ||
| methods.loadModel = function ( json ) { | ||
| // Used to check presence of required fields; `uid` is checked separately. | ||
| const modelFields = [ 'docId', 'tf', 'idf', 'terms', 'sumOfAllDLs' ]; | ||
| let model; | ||
| if ( docId > 0 ) throw Error( 'wink-nlp: can not load model after learning.' ); | ||
| try { | ||
| model = JSON.parse( json ); | ||
| } catch (e) { | ||
| throw Error( `wink-nlp: invalid input JSON:\n\t${e}\n\n` ); | ||
| } | ||
| if ( helper.isObject( model ) && ( Object.keys( model ).length === 6 ) && ( model.uid === 'WinkNLP-BM25Vectorizer-Model/1.0.0' ) ) { | ||
| // Check presence of all required fields. | ||
| modelFields.forEach( ( f ) => { | ||
| if ( model[ f ] === undefined ) throw Error( 'wink-nlp: invalid model format/version' ); | ||
| } ); | ||
| // All good, set fields. | ||
| docId = model.docId; | ||
| tf = model.tf; | ||
| idf = model.idf; | ||
| terms = model.terms; | ||
| sumOfAllDLs = model.sumOfAllDLs; | ||
| // To prevent further learning. | ||
| weightsComputed = true; | ||
| } else { | ||
| throw Error( 'wink-nlp: invalid model format/version' ); | ||
| } | ||
| }; // loadModel() | ||
| return methods; | ||
@@ -324,0 +359,0 @@ }; // bm25Vectorizer() |
Sorry, the diff of this file is too big to display
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Shell access
Supply chain riskThis module accesses the system shell. Accessing the system shell increases the risk of executing arbitrary code.
Found 1 instance in 1 package
Long strings
Supply chain riskContains long string literals, which may be a sign of obfuscated or packed code.
Found 1 instance in 1 package
Shell access
Supply chain riskThis module accesses the system shell. Accessing the system shell increases the risk of executing arbitrary code.
Found 1 instance in 1 package
Long strings
Supply chain riskContains long string literals, which may be a sign of obfuscated or packed code.
Found 1 instance in 1 package
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1.3%19
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