Update README.md

README.md

@@ -72,6 +72,33 @@ print("Sentence embeddings:")
 print(sentence_embeddings)
 ```
 
+## Usage (Text Embeddings Inference (TEI))
+
+[Text Embeddings Inference (TEI)](https://github.com/huggingface/text-embeddings-inference) is a blazing fast inference solution for text embeddings models.
+
+- CPU:
+```bash
+docker run -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-latest --model-id sentence-transformers/all-mpnet-base-v1 --pooling mean --dtype float16
+```
+
+- NVIDIA GPU:
+```bash
+docker run --gpus all -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cuda-latest --model-id sentence-transformers/all-mpnet-base-v1 --pooling mean --dtype float16
+```
+
+Send a request to `/v1/embeddings` to generate embeddings via the [OpenAI Embeddings API](https://platform.openai.com/docs/api-reference/embeddings/create):
+```bash
+curl http://localhost:8080/v1/embeddings \
+  -X POST \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "sentence-transformers/all-mpnet-base-v1",
+    "input": ["This is an example sentence", "Each sentence is converted"]
+  }'
+```
+
+Or check the [Text Embeddings Inference API specification](https://huggingface.github.io/text-embeddings-inference/) instead.
+
 ------
 
 ## Background
@@ -92,7 +119,6 @@ the semantic information. The sentence vector may be used for information retrie
 
 By default, input text longer than 128 word pieces is truncated.
 
-
 ## Training procedure
 
 ### Pre-training 
@@ -148,31 +174,4 @@ We sampled each dataset given a weighted probability which configuration is deta
 | [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
 | [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
 | [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
-| **Total** | | **1,124,818,467** |
-
-## Usage (Text Embeddings Inference (TEI))
-
-[Text Embeddings Inference (TEI)](https://github.com/huggingface/text-embeddings-inference) is a blazing fast inference solution for text embeddings models.
-
-- CPU:
-```bash
-docker run -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-latest --model-id sentence-transformers/all-mpnet-base-v1 --pooling mean --dtype float16
-```
-
-- NVIDIA GPU:
-```bash
-docker run --gpus all -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cuda-latest --model-id sentence-transformers/all-mpnet-base-v1 --pooling mean --dtype float16
-```
-
-Send a request to `/v1/embeddings` to generate embeddings via the [OpenAI Embeddings API](https://platform.openai.com/docs/api-reference/embeddings/create):
-```bash
-curl http://localhost:8080/v1/embeddings \
-  -X POST \
-  -H "Content-Type: application/json" \
-  -d '{
-    "model": "sentence-transformers/all-mpnet-base-v1",
-    "input": ["This is an example sentence", "Each sentence is converted"]
-  }'
-```
-
-Or check the [Text Embeddings Inference API specification](https://huggingface.github.io/text-embeddings-inference/) instead.
+| **Total** | | **1,124,818,467** |