Add Transformers.js tags + sample code

README.md

@@ -8,6 +8,7 @@ pipeline_tag: sentence-similarity
 library_name: transformers
 tags:
 - sentence-transformers
+- transformers.js
 ---
 
 # gte-reranker-modernbert-base
@@ -96,6 +97,37 @@ print(scores)
 # NOTE: Sentence Transformers calls Softmax over the outputs by default, hence the scores are in [0, 1] range.
 ```
 
+Use with `transformers.js`
+```js
+import {
+  AutoTokenizer,
+  AutoModelForSequenceClassification,
+} from "@huggingface/transformers";
+
+const model_id = "Alibaba-NLP/gte-reranker-modernbert-base";
+const model = await AutoModelForSequenceClassification.from_pretrained(
+  model_id,
+  { dtype: "fp32" }, // Supported options: "fp32", "fp16", "q8", "q4", "q4f16"
+);
+const tokenizer = await AutoTokenizer.from_pretrained(model_id);
+
+const pairs = [
+  ["what is the capital of China?", "Beijing"],
+  ["how to implement quick sort in python?", "Introduction of quick sort"],
+  ["how to implement quick sort in python?", "The weather is nice today"],
+];
+const inputs = tokenizer(
+  pairs.map((x) => x[0]),
+  {
+    text_pair: pairs.map((x) => x[1]),
+    padding: true,
+    truncation: true,
+  },
+);
+const { logits } = await model(inputs);
+console.log(logits.tolist()); // [[2.138258218765259], [2.4609625339508057], [-1.6775450706481934]]
+```
+
 ## Training Details
 
 The `gte-modernbert` series of models follows the training scheme of the previous [GTE models](https://huggingface.co/collections/Alibaba-NLP/gte-models-6680f0b13f885cb431e6d469), with the only difference being that the pre-training language model base has been replaced from [GTE-MLM](https://huggingface.co/Alibaba-NLP/gte-en-mlm-base) to [ModernBert](https://huggingface.co/answerdotai/ModernBERT-base). For more training details, please refer to our paper: [mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval](https://aclanthology.org/2024.emnlp-industry.103/)