diff --git a/src/pipelines.js b/src/pipelines.js
index 2b064d522..3b15af53d 100644
--- a/src/pipelines.js
+++ b/src/pipelines.js
@@ -67,6 +67,7 @@ import {
     Tensor,
     mean_pooling,
     interpolate,
+    quantize_embeddings,
 } from './utils/tensor.js';
 import { RawImage } from './utils/image.js';
 
@@ -1112,6 +1113,8 @@ export class ZeroShotClassificationPipeline extends (/** @type {new (options: Te
  * @typedef {Object} FeatureExtractionPipelineOptions Parameters specific to feature extraction pipelines.
  * @property {'none'|'mean'|'cls'} [pooling="none"] The pooling method to use.
  * @property {boolean} [normalize=false] Whether or not to normalize the embeddings in the last dimension.
+ * @property {boolean} [quantize=false] Whether or not to quantize the embeddings.
+ * @property {'binary'|'ubinary'} [precision='binary'] The precision to use for quantization. 
  * 
  * @callback FeatureExtractionPipelineCallback Extract the features of the input(s).
  * @param {string|string[]} texts One or several texts (or one list of texts) to get the features of.
@@ -1157,6 +1160,16 @@ export class ZeroShotClassificationPipeline extends (/** @type {new (options: Te
  * //   dims: [1, 384]
  * // }
  * ```
+ * **Example:** Calculating binary embeddings with `sentence-transformers` models.
+ * ```javascript
+ * const extractor = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');
+ * const output = await extractor('This is a simple test.', { pooling: 'mean', quantize: true, precision: 'binary' });
+ * // Tensor {
+ * //   type: 'int8',
+ * //   data: Int8Array [49, 108, 24, ...],
+ * //   dims: [1, 48]
+ * // }
+ * ```
  */
 export class FeatureExtractionPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => FeatureExtractionPipelineType} */ (Pipeline)) {
     /**
@@ -1171,6 +1184,8 @@ export class FeatureExtractionPipeline extends (/** @type {new (options: TextPip
     async _call(texts, {
         pooling = /** @type {'none'} */('none'),
         normalize = false,
+        quantize = false,
+        precision = /** @type {'binary'} */('binary'),
     } = {}) {
 
         // Run tokenization
@@ -1203,6 +1218,10 @@ export class FeatureExtractionPipeline extends (/** @type {new (options: TextPip
             result = result.normalize(2, -1);
         }
 
+        if (quantize) {
+            result = quantize_embeddings(result, precision);
+        }
+
         return result;
     }
 }
diff --git a/src/tokenizers.js b/src/tokenizers.js
index 5b58e37c0..e671c8318 100644
--- a/src/tokenizers.js
+++ b/src/tokenizers.js
@@ -2653,8 +2653,8 @@ export class PreTrainedTokenizer extends Callable {
             }
 
         } else {
-            if (text === null) {
-                throw Error('text may not be null')
+            if (text === null || text === undefined) {
+                throw Error('text may not be null or undefined')
             }
 
             if (Array.isArray(text_pair)) {
diff --git a/src/utils/tensor.js b/src/utils/tensor.js
index ccdf781be..469054cac 100644
--- a/src/utils/tensor.js
+++ b/src/utils/tensor.js
@@ -1193,3 +1193,47 @@ export function ones(size) {
 export function ones_like(tensor) {
     return ones(tensor.dims);
 }
+
+/**
+ * Quantizes the embeddings tensor to binary or unsigned binary precision.
+ * @param {Tensor} tensor The tensor to quantize.
+ * @param {'binary'|'ubinary'} precision The precision to use for quantization.
+ * @returns {Tensor} The quantized tensor.
+ */
+export function quantize_embeddings(tensor, precision) {
+    if (tensor.dims.length !== 2) {
+        throw new Error("The tensor must have 2 dimensions");
+    }
+    if (tensor.dims.at(-1) % 8 !== 0) {
+        throw new Error("The last dimension of the tensor must be a multiple of 8");
+    }
+    if (!['binary', 'ubinary'].includes(precision)) {
+        throw new Error("The precision must be either 'binary' or 'ubinary'");
+    }
+
+    const signed = precision === 'binary';
+    const dtype = signed ? 'int8' : 'uint8';
+
+    // Create a typed array to store the packed bits
+    const cls = signed ? Int8Array : Uint8Array;
+    const inputData = tensor.data;
+    const outputData = new cls(inputData.length / 8);
+
+    // Iterate over each number in the array
+    for (let i = 0; i < inputData.length; ++i) {
+        // Determine if the number is greater than 0
+        const bit = inputData[i] > 0 ? 1 : 0;
+
+        // Calculate the index in the typed array and the position within the byte
+        const arrayIndex = Math.floor(i / 8);
+        const bitPosition = i % 8;
+
+        // Pack the bit into the typed array
+        outputData[arrayIndex] |= bit << (7 - bitPosition);
+        if (signed && bitPosition === 0) {
+            outputData[arrayIndex] -= 128;
+        }
+    };
+
+    return new Tensor(dtype, outputData, [tensor.dims[0], tensor.dims[1] / 8]);
+}