Add `text-embeddings-inference` tag & snippet (#4)

- Add `text-embeddings-inference` tag & snippet (c99f96f79723ad601d3fa7f73a4d57c40d86f4a0)
- Update README.md (645fd273cf0144000b28ba5c2a64ce27c77520b5)
- embeddings models -> embedding models (799acf92da6b8e71dc136ad470dfe4eb89b4726e)


Co-authored-by: Alvaro Bartolome <[email protected]>

README.md

@@ -6,6 +6,7 @@ tags:
 - feature-extraction
 - sentence-similarity
 - transformers
+- text-embeddings-inference
 pipeline_tag: sentence-similarity
 ---
 
@@ -44,9 +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)
 
@@ -65,14 +66,35 @@ encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tenso
 with torch.no_grad():
     model_output = model(**encoded_input)
 
-# Perform pooling. In this case, max pooling.
+# Perform pooling. In this case, mean pooling.
 sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
 
 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/nli-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/nli-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/nli-mpnet-base-v2","input":"This is an example sentence"}'
+```
+
+Or check the [Text Embeddings Inference API specification](https://huggingface.github.io/text-embeddings-inference/) instead.
 
 ## Full Model Architecture
 ```