fix linting issues

Ingvarstep · Oct 8, 2024 · 9001e24 · 9001e24
1 parent 748347b
commit 9001e24
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Showing 4 changed files with 67 additions and 54 deletions.
diff --git a/src/Gliner.ts b/src/Gliner.ts
@@ -44,7 +44,11 @@ export class Gliner {
     env.allowLocalModels = config.transformersSettings?.allowLocalModels ?? false;
     env.useBrowserCache = config.transformersSettings?.useBrowserCache ?? false;
 
-    this.config = { ...config, maxWidth: config.maxWidth || 12, modelType: config.modelType || "span-level" };
+    this.config = {
+      ...config,
+      maxWidth: config.maxWidth || 12,
+      modelType: config.modelType || "span-level",
+    };
   }
 
   async initialize(): Promise<void> {
@@ -62,8 +66,7 @@ export class Gliner {
       const decoder = new SpanDecoder({ max_width: maxWidth });
 
       this.model = new SpanModel({ max_width: maxWidth }, processor, decoder, onnxWrapper);
-    }
-    else {
+    } else {
       console.log("Initializing Token-level Model...");
 
       const processor = new TokenProcessor({ max_width: maxWidth }, tokenizer, wordSplitter);
@@ -79,7 +82,7 @@ export class Gliner {
     entities,
     flatNer = true,
     threshold = 0.5,
-    multiLabel = false
+    multiLabel = false,
   }: IInference): Promise<InferenceResultMultiple> {
     if (!this.model) {
       throw new Error("Model is not initialized. Call initialize() first.");

diff --git a/src/decoder.ts b/src/decoder.ts
@@ -57,7 +57,7 @@ abstract class BaseDecoder {
 
     const newList: Spans = [];
     // Sort spans by their score (last element) in descending order
-    const spanProb: Spans = spans.slice().sort((a, b) => b[b.length-1] - a[a.length - 1]);
+    const spanProb: Spans = spans.slice().sort((a, b) => b[b.length - 1] - a[a.length - 1]);
 
     for (let i = 0; i < spanProb.length; i++) {
       const b = spanProb[i];
@@ -77,7 +77,7 @@ abstract class BaseDecoder {
     // Sort newList by start position (second element) for correct ordering
     return newList.sort((a, b) => a[1] - b[1]);
   }
-};
+}
 // SpanDecoder subclass
 export class SpanDecoder extends BaseDecoder {
   decode(
@@ -95,7 +95,7 @@ export class SpanDecoder extends BaseDecoder {
     threshold: number = 0.5,
     multiLabel: boolean = false,
   ): RawInferenceResult {
-    const spans: RawInferenceResult= [];
+    const spans: RawInferenceResult = [];
 
     for (let batch = 0; batch < batchSize; batch++) {
       spans.push([]);
@@ -152,7 +152,6 @@ export class TokenDecoder extends BaseDecoder {
     threshold: number = 0.5,
     multiLabel: boolean = false,
   ): RawInferenceResult {
-
     const positionPadding = batchSize * inputLength * numEntities;
     const batchPadding = inputLength * numEntities;
     const tokenPadding = numEntities;
@@ -203,10 +202,10 @@ export class TokenDecoder extends BaseDecoder {
             // Calculate the inside span scores
             const insideSpanScores = insideScore[batch]
               .slice(start, end + 1)
-              .map(tokenScores => tokenScores[clsSt]);
+              .map((tokenScores) => tokenScores[clsSt]);
 
             // Check if all scores within the span are above the threshold
-            if (insideSpanScores.some(score => score < threshold)) continue;
+            if (insideSpanScores.some((score) => score < threshold)) continue;
 
             // Calculate mean span score
             const spanScore = insideSpanScores.reduce((a, b) => a + b, 0) / insideSpanScores.length;
@@ -233,4 +232,4 @@ export class TokenDecoder extends BaseDecoder {
 
     return allSelectedSpans;
   }
-}
+}
diff --git a/src/model.ts b/src/model.ts
@@ -13,7 +13,7 @@ export class Model {
   async initialize(): Promise<void> {
     await this.onnxWrapper.init();
   }
-};
+}
 
 export class SpanModel extends Model {
   prepareInputs(batch: any): Record<string, ort.Tensor> {
@@ -91,18 +91,22 @@ export class SpanModel extends Model {
     batch_size: number = 4,
     max_words: number = 512,
   ): Promise<RawInferenceResult> {
-    const { idToClass }: { 
-          idToClass: Record<number, string> } = this.processor.createMappings(entities);
+    const {
+      idToClass,
+    }: {
+      idToClass: Record<number, string>;
+    } = this.processor.createMappings(entities);
     let batchIds: number[] = [];
     let batchTokens: string[][] = [];
     let batchWordsStartIdx: number[][] = [];
     let batchWordsEndIdx: number[][] = [];
     texts.forEach((text, id) => {
-      let [tokens, wordsStartIdx, wordsEndIdx]: [string[], number[], number[]] = this.processor.tokenizeText(text);
+      let [tokens, wordsStartIdx, wordsEndIdx]: [string[], number[], number[]] =
+        this.processor.tokenizeText(text);
       let num_sub_batches: number = Math.ceil(tokens.length / max_words);
 
       for (let i = 0; i < num_sub_batches; i++) {
-        let start:number = i * max_words;
+        let start: number = i * max_words;
         let end: number = Math.min((i + 1) * max_words, tokens.length);
 
         batchIds.push(id);
@@ -128,24 +132,18 @@ export class SpanModel extends Model {
       let currBatchWordsEndIdx: number[][] = batchWordsEndIdx.slice(start, end);
       let currBatchIds: number[] = batchIds.slice(start, end);
 
-      let [inputTokens, textLengths, promptLengths]: [string[][], number[], number[]] = this.processor.prepareTextInputs(
-        currBatchTokens,
-        entities,
-      );
+      let [inputTokens, textLengths, promptLengths]: [string[][], number[], number[]] =
+        this.processor.prepareTextInputs(currBatchTokens, entities);
 
-      let [inputsIds, attentionMasks, wordsMasks]: [number[][], number[][], number[][]] = this.processor.encodeInputs(
-        inputTokens,
-        promptLengths,
-      );
+      let [inputsIds, attentionMasks, wordsMasks]: [number[][], number[][], number[][]] =
+        this.processor.encodeInputs(inputTokens, promptLengths);
 
       inputsIds = this.processor.padArray(inputsIds);
       attentionMasks = this.processor.padArray(attentionMasks);
       wordsMasks = this.processor.padArray(wordsMasks);
 
-      let { spanIdxs, spanMasks }: { spanIdxs: number[][][]; spanMasks: boolean[][] } = this.processor.prepareSpans(
-        currBatchTokens,
-        this.config["max_width"],
-      );
+      let { spanIdxs, spanMasks }: { spanIdxs: number[][][]; spanMasks: boolean[][] } =
+        this.processor.prepareSpans(currBatchTokens, this.config["max_width"]);
 
       spanIdxs = this.processor.padArray(spanIdxs, 3);
       spanMasks = this.processor.padArray(spanMasks);
@@ -164,7 +162,7 @@ export class SpanModel extends Model {
       };
 
       let feeds: Record<string, ort.Tensor> = this.prepareInputs(batch);
-      const results: Record<string, ort.Tensor>  = await this.onnxWrapper.run(feeds);
+      const results: Record<string, ort.Tensor> = await this.onnxWrapper.run(feeds);
       const modelOutput: number[] = results["logits"].data;
       // const modelOutput = results.logits.data as number[];
 
@@ -267,18 +265,22 @@ export class TokenModel extends Model {
     batch_size: number = 4,
     max_words: number = 512,
   ): Promise<RawInferenceResult> {
-    const { idToClass }: { 
-          idToClass: Record<number, string> } = this.processor.createMappings(entities);
+    const {
+      idToClass,
+    }: {
+      idToClass: Record<number, string>;
+    } = this.processor.createMappings(entities);
     let batchIds: number[] = [];
     let batchTokens: string[][] = [];
     let batchWordsStartIdx: number[][] = [];
     let batchWordsEndIdx: number[][] = [];
     texts.forEach((text, id) => {
-      let [tokens, wordsStartIdx, wordsEndIdx]: [string[], number[], number[]] = this.processor.tokenizeText(text);
+      let [tokens, wordsStartIdx, wordsEndIdx]: [string[], number[], number[]] =
+        this.processor.tokenizeText(text);
       let num_sub_batches: number = Math.ceil(tokens.length / max_words);
 
       for (let i = 0; i < num_sub_batches; i++) {
-        let start:number = i * max_words;
+        let start: number = i * max_words;
         let end: number = Math.min((i + 1) * max_words, tokens.length);
 
         batchIds.push(id);
@@ -304,15 +306,11 @@ export class TokenModel extends Model {
       let currBatchWordsEndIdx: number[][] = batchWordsEndIdx.slice(start, end);
       let currBatchIds: number[] = batchIds.slice(start, end);
 
-      let [inputTokens, textLengths, promptLengths]: [string[][], number[], number[]] = this.processor.prepareTextInputs(
-        currBatchTokens,
-        entities,
-      );
+      let [inputTokens, textLengths, promptLengths]: [string[][], number[], number[]] =
+        this.processor.prepareTextInputs(currBatchTokens, entities);
 
-      let [inputsIds, attentionMasks, wordsMasks]: [number[][], number[][], number[][]] = this.processor.encodeInputs(
-        inputTokens,
-        promptLengths,
-      );
+      let [inputsIds, attentionMasks, wordsMasks]: [number[][], number[][], number[][]] =
+        this.processor.encodeInputs(inputTokens, promptLengths);
 
       inputsIds = this.processor.padArray(inputsIds);
       attentionMasks = this.processor.padArray(attentionMasks);
@@ -330,7 +328,7 @@ export class TokenModel extends Model {
       };
 
       let feeds: Record<string, ort.Tensor> = this.prepareInputs(batch);
-      const results: Record<string, ort.Tensor>  = await this.onnxWrapper.run(feeds);
+      const results: Record<string, ort.Tensor> = await this.onnxWrapper.run(feeds);
       const modelOutput: number[] = results["logits"].data;
       // const modelOutput = results.logits.data as number[];
 
@@ -361,4 +359,4 @@ export class TokenModel extends Model {
 
     return finalDecodedSpans;
   }
-}
+}
diff --git a/src/processor.ts b/src/processor.ts
@@ -181,17 +181,24 @@ export class SpanProcessor extends Processor {
   }
 
   prepareBatch(texts: string[], entities: string[]): Record<string, any> {
-    const [batchTokens, batchWordsStartIdx, batchWordsEndIdx]: [string[][], number[][], number[][]] = this.batchTokenizeText(texts);
-    const { idToClass }:  {idToClass: Record<number, string>} = this.createMappings(entities);
-    const [inputTokens, textLengths, promptLengths]:  [string[][], number[], number[]] = this.prepareTextInputs(batchTokens, entities);
-
-    let [inputsIds, attentionMasks, wordsMasks]: [number[][], number[][], number[][]] = this.encodeInputs(inputTokens, promptLengths);
+    const [batchTokens, batchWordsStartIdx, batchWordsEndIdx]: [
+      string[][],
+      number[][],
+      number[][],
+    ] = this.batchTokenizeText(texts);
+    const { idToClass }: { idToClass: Record<number, string> } = this.createMappings(entities);
+    const [inputTokens, textLengths, promptLengths]: [string[][], number[], number[]] =
+      this.prepareTextInputs(batchTokens, entities);
+
+    let [inputsIds, attentionMasks, wordsMasks]: [number[][], number[][], number[][]] =
+      this.encodeInputs(inputTokens, promptLengths);
 
     inputsIds = this.padArray(inputsIds);
     attentionMasks = this.padArray(attentionMasks);
     wordsMasks = this.padArray(wordsMasks);
 
-    let { spanIdxs, spanMasks }: { spanIdxs: number[][][]; spanMasks: boolean[][] } = this.prepareSpans(batchTokens, this.config["max_width"]);
+    let { spanIdxs, spanMasks }: { spanIdxs: number[][][]; spanMasks: boolean[][] } =
+      this.prepareSpans(batchTokens, this.config["max_width"]);
 
     spanIdxs = this.padArray(spanIdxs, 3);
     spanMasks = this.padArray(spanMasks);
@@ -217,11 +224,17 @@ export class TokenProcessor extends Processor {
   }
 
   prepareBatch(texts: string[], entities: string[]): Record<string, any> {
-    const [batchTokens, batchWordsStartIdx, batchWordsEndIdx]: [string[][], number[][], number[][]] = this.batchTokenizeText(texts);
-    const { idToClass }:  {idToClass: Record<number, string>} = this.createMappings(entities);
-    const [inputTokens, textLengths, promptLengths]:  [string[][], number[], number[]] = this.prepareTextInputs(batchTokens, entities);
-
-    let [inputsIds, attentionMasks, wordsMasks]: [number[][], number[][], number[][]] = this.encodeInputs(inputTokens, promptLengths);
+    const [batchTokens, batchWordsStartIdx, batchWordsEndIdx]: [
+      string[][],
+      number[][],
+      number[][],
+    ] = this.batchTokenizeText(texts);
+    const { idToClass }: { idToClass: Record<number, string> } = this.createMappings(entities);
+    const [inputTokens, textLengths, promptLengths]: [string[][], number[], number[]] =
+      this.prepareTextInputs(batchTokens, entities);
+
+    let [inputsIds, attentionMasks, wordsMasks]: [number[][], number[][], number[][]] =
+      this.encodeInputs(inputTokens, promptLengths);
 
     inputsIds = this.padArray(inputsIds);
     attentionMasks = this.padArray(attentionMasks);