diff --git a/examples/sentiment-analysis/analyzer.ts b/examples/sentiment-analysis/analyzer.ts
index 6ae951b..c3b3d9c 100644
--- a/examples/sentiment-analysis/analyzer.ts
+++ b/examples/sentiment-analysis/analyzer.ts
@@ -1,69 +1,54 @@
 import { CPU, setupBackend, tensor } from "jsr:@denosaurs/netsaur@0.4.0";
 import { Sequential } from "jsr:@denosaurs/netsaur@0.4.0/core";
 
-import type {
-  MatrixLike,
-} from "jsr:@denosaurs/netsaur@0.4.0/utilities";
+import type { MatrixLike } from "jsr:@denosaurs/netsaur@0.4.0/utilities";
 
 import { CategoricalEncoder } from "jsr:@denosaurs/netsaur@0.4.0/utilities/encoding";
 import {
-  CountVectorizer,
-  SplitTokenizer,
+    CountVectorizer,
+    SplitTokenizer,
+    TfIdfTransformer,
 } from "jsr:@denosaurs/netsaur@0.4.0/utilities/text";
 
-import Mappings from "./mappings.json" with {type: "json"}
-import Vocab from "./vocab.json" with {type: "json"}
-import Idf from "./idf.json" with {type: "json"}
-
-
-console.time("Time Elapsed");
-
-console.log("\nImports loaded.");
-
+import Mappings from "./mappings.json" with { type: "json" };
+import Vocab from "./vocab.json" with { type: "json" };
+import Idf from "./tfidf.json" with { type: "json" };
 
 const vocab = new Map();
 
 for (const entry of Vocab) {
-    vocab.set(entry[0], entry[1])
+    vocab.set(entry[0], entry[1]);
 }
 
 const tokenizer = new SplitTokenizer({
-  skipWords: "english",
-  vocabulary: vocab,
-  standardize: { lowercase: true, stripNewlines: true },
+    skipWords: "english",
+    vocabulary: vocab,
+    standardize: { lowercase: true, stripNewlines: true },
 });
 
 const vectorizer = new CountVectorizer(tokenizer.vocabulary.size);
-
-console.log("\nX vectorized");
-console.timeLog("Time Elapsed");
+const transformer = new TfIdfTransformer({ idf: Float64Array.from(Idf) });
 
 const encoder = new CategoricalEncoder<string>();
 const mappings = new Map();
 
 for (const entry of Mappings) {
-    mappings.set(entry[0], entry[1])
+    mappings.set(entry[0], entry[1]);
 }
 
 encoder.mapping = mappings;
 
-console.log("\nCPU Backend Loading");
-console.timeLog("Time Elapsed");
-
 await setupBackend(CPU);
 
-console.log("\nCPU Backend Loaded");
-console.timeLog("Time Elapsed");
-
-const net = Sequential.loadFile("examples/sentiment-analysis/sentiment.st")
+const net = Sequential.loadFile("examples/sentiment-analysis/sentiment.st");
 
-const text = prompt("Text to analyze?") || "hello world"
+const text = prompt("Text to analyze?") || "hello world";
 
 const predYSoftmax = await net.predict(
-  tensor(vectorizer.transform(tokenizer.transform([text]), "f32"))
+    tensor(transformer.transform<"f32">(vectorizer.transform(tokenizer.transform([text]), "f32"))),
 );
 
 CategoricalEncoder.fromSoftmax<"f32">(predYSoftmax as MatrixLike<"f32">);
 const predY = encoder.untransform(predYSoftmax as MatrixLike<"f32">);
 
-console.log(`The sentiment predicted is ${predY[0]}`)
\ No newline at end of file
+console.log(`The sentiment predicted is ${predY[0]}`);
diff --git a/examples/sentiment-analysis/tester.ts b/examples/sentiment-analysis/tester.ts
index ecd5d4d..ce8656e 100644
--- a/examples/sentiment-analysis/tester.ts
+++ b/examples/sentiment-analysis/tester.ts
@@ -2,92 +2,76 @@ import { CPU, setupBackend, tensor } from "jsr:@denosaurs/netsaur@0.4.0";
 import { Sequential } from "jsr:@denosaurs/netsaur@0.4.0/core";
 
 import {
-  useSplit,
-  ClassificationReport,
-  type MatrixLike,
+    ClassificationReport,
+    type MatrixLike,
+    useSplit,
 } from "jsr:@denosaurs/netsaur@0.4.0/utilities";
 
 import { CategoricalEncoder } from "jsr:@denosaurs/netsaur@0.4.0/utilities/encoding";
 import {
-  CountVectorizer,
-  SplitTokenizer,
+    CountVectorizer,
+    SplitTokenizer,
+    TfIdfTransformer,
 } from "jsr:@denosaurs/netsaur@0.4.0/utilities/text";
 
-import Mappings from "./mappings.json" with {type: "json"}
-import Vocab from "./vocab.json" with {type: "json"}
-import Idf from "./idf.json" with {type: "json"}
+import Mappings from "./mappings.json" with { type: "json" };
+import Vocab from "./vocab.json" with { type: "json" };
+import Idf from "./tfidf.json" with { type: "json" };
 
 import { parse as parseCsv } from "jsr:@std/csv@1.0.3/parse";
 
-
-console.time("Time Elapsed");
-
-console.log("\nImports loaded.");
-
 const file = Deno.readTextFileSync(
-    "examples/sentiment-analysis/text_emotion.csv"
-  );
-  
-  console.log("\nData file loaded.");
-  console.timeLog("Time Elapsed");
-  
-  const data = parseCsv(file, { skipFirstRow: true }) as {
+    "examples/sentiment-analysis/text_emotion.csv",
+);
+
+const data = parseCsv(file, { skipFirstRow: true }) as {
     sentiment: string;
     content: string;
-  }[];
-  const text = data.map((x) => x.content);
+}[];
+const text = data.map((x) => x.content);
 const labels = data.map((x) => x.sentiment);
 
-console.log("\nCSV Parsed");
-console.timeLog("Time Elapsed");
-
 const [[_trainX, _trainY], [testX, testY]] = useSplit(
-  { shuffle: true, ratio: [7, 3] },
-  text,
-  labels
+    { shuffle: true, ratio: [7, 3] },
+    text,
+    labels,
 );
 
-console.log("Data Split");
-console.timeLog("Time Elapsed");
-
 const vocab = new Map();
 
 for (const entry of Vocab) {
-    vocab.set(entry[0], entry[1])
+    vocab.set(entry[0], entry[1]);
 }
 
 const tokenizer = new SplitTokenizer({
-  skipWords: "english",
-  vocabulary: vocab,
-  standardize: { lowercase: true, stripNewlines: true },
+    skipWords: "english",
+    vocabulary: vocab,
+    standardize: { lowercase: true, stripNewlines: true },
 });
 
 const vectorizer = new CountVectorizer(tokenizer.vocabulary.size);
 
-console.log("\nX vectorized");
-console.timeLog("Time Elapsed");
+const transformer = new TfIdfTransformer({ idf: Float64Array.from(Idf) });
 
 const encoder = new CategoricalEncoder<string>();
 const mappings = new Map();
 
 for (const entry of Mappings) {
-    mappings.set(entry[0], entry[1])
+    mappings.set(entry[0], entry[1]);
 }
 
 encoder.mapping = mappings;
 
-console.log("\nCPU Backend Loading");
-console.timeLog("Time Elapsed");
-
 await setupBackend(CPU);
 
-console.log("\nCPU Backend Loaded");
-console.timeLog("Time Elapsed");
-
-const net = Sequential.loadFile("examples/sentiment-analysis/sentiment.st")
+const net = Sequential.loadFile("examples/sentiment-analysis/sentiment.st");
 
 const predYSoftmax = await net.predict(
-  tensor(vectorizer.transform(tokenizer.transform(testX), "f32"))
+    tensor(
+        transformer.transform<"f32">(
+            vectorizer.transform(tokenizer.transform(testX), "f32"),
+        ),
+    ),
 );
 
 CategoricalEncoder.fromSoftmax<"f32">(predYSoftmax as MatrixLike<"f32">);
@@ -95,4 +79,4 @@ const predY = encoder.untransform(predYSoftmax as MatrixLike<"f32">);
 
 console.log(new ClassificationReport(testY, predY));
 
-console.log(testY, predY)
\ No newline at end of file
+console.log(testY, predY);