Add Text Embeddings Inference (TEI) tag & snippet (#17)

- Add Text Embeddings Inference (TEI) tag & snippet (8afed724dfb37107ce6b9a63a8aff016919bdb24)


Co-authored-by: Alvaro Bartolome <alvarobartt@users.noreply.huggingface.co>

README.md

@@ -12,6 +12,7 @@ tags:
 - mteb
 - embedding
 - transformers.js
+- text-embeddings-inference
 ---
 
 # gte-modernbert-base
@@ -131,6 +132,47 @@ const similarities = (await matmul(embeddings.slice([0, 1]), embeddings.slice([1
 console.log(similarities.tolist()); // [[42.89077377319336, 71.30916595458984, 33.66455841064453]]
 ```
 
+Additionally, you can also deploy `Alibaba-NLP/gte-modernbert-base` with [Text Embeddings Inference (TEI)](https://github.com/huggingface/text-embeddings-inference) as follows:
+
+- CPU
+
+```bash
+docker run --platform linux/amd64 \
+  -p 8080:80 \
+  -v $PWD/data:/data \
+  --pull always \
+  ghcr.io/huggingface/text-embeddings-inference:cpu-1.7 \
+  --model-id Alibaba-NLP/gte-modernbert-base
+```
+
+- GPU
+
+```bash
+docker run --gpus all \
+  -p 8080:80 \
+  -v $PWD/data:/data \
+  --pull always \
+  ghcr.io/huggingface/text-embeddings-inference:1.7 \
+  --model-id Alibaba-NLP/gte-modernbert-base
+```
+
+Then you can send requests to the deployed API via the OpenAI-compatible `v1/embeddings` route (more information about the [OpenAI Embeddings API](https://platform.openai.com/docs/api-reference/embeddings)):
+
+```bash
+curl https://0.0.0.0:8080/v1/embeddings \
+  -H "Content-Type: application/json" \
+  -d '{
+    "input": [
+      "what is the capital of China?",
+      "how to implement quick sort in python?",
+      "Beijing",
+      "sorting algorithms"
+    ],
+    "model": "Alibaba-NLP/gte-modernbert-base",
+    "encoding_format": "float"
+  }'
+```
+
 ## 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/)