Update README.md

README.md

@@ -28,12 +28,12 @@ You can then use the model to compute embeddings, as follows:
 import { pipeline, cos_sim } from '@xenova/transformers';
 
 // Create a feature extraction pipeline
-const extractor = await pipeline('feature-extraction', 'Xenova/jina-embeddings-v2-base-zh', {
+const extractor = await pipeline('feature-extraction', 'Xenova/jina-embeddings-v2-base-de', {
     quantized: false, // Comment out this line to use the quantized version
 });
 
 // Compute sentence embeddings
-const texts = ['How is the weather today?', '今天天气怎么样?'];
+const texts = ['How is the weather today?', 'Wie ist das Wetter heute?'];
 const output = await extractor(texts, { pooling: 'mean', normalize: true });
 // Tensor {
 //   dims: [2, 768],
@@ -45,10 +45,9 @@ const output = await extractor(texts, { pooling: 'mean', normalize: true });
 // Compute cosine similarity between the two embeddings
 const score = cos_sim(output[0].data, output[1].data);
 console.log(score);
-// 0.7860610759096025
+// 0.9602110344414481
 ```
 
 ---
 
 Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
-