move Usage, embeddings models -> embedding models

README.md

@@ -45,9 +45,9 @@ from transformers import AutoTokenizer, AutoModel
 import torch
 
 
-#Mean Pooling - Take attention mask into account for correct averaging
+# Mean Pooling - Take attention mask into account for correct averaging
 def mean_pooling(model_output, attention_mask):
-    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    token_embeddings = model_output[0] # First element of model_output contains all token embeddings
     input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
     return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
 
@@ -73,7 +73,31 @@ 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 embedding 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/stsb-mpnet-base-v2 --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/stsb-mpnet-base-v2 --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 \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "sentence-transformers/stsb-mpnet-base-v2",
+    "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.
 
 ## Full Model Architecture
 ```
@@ -99,29 +123,3 @@ If you find this model helpful, feel free to cite our publication [Sentence-BERT
     url = "http://arxiv.org/abs/1908.10084",
 }
 ```
-
-## 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/stsb-mpnet-base-v2 --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/stsb-mpnet-base-v2 --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 \
-  -H "Content-Type: application/json" \
-  -d '{
-    "model": "sentence-transformers/stsb-mpnet-base-v2",
-    "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.