Update README with custom snippet

README.md

@@ -9,53 +9,29 @@ license: apache-2.0
 ---
 # ONNX convert all-MiniLM-L6-v2
 ## Conversion of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
-## Usage (Sentence-Transformers)
-Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+This is a [sentence-transformers](https://www.SBERT.net) ONNX model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. This custom model takes `last_hidden_state` and `pooler_output` whereas the sentence-transformers exported with default ONNX config only contains `last_hidden_state` as output.
+
+## Usage (HuggingFace Optimum)
+Using this model becomes easy when you have [optimum](https://github.com/huggingface/optimum) installed:
 ```
-pip install -U sentence-transformers
+python -m pip install optimum
 ```
 Then you can use the model like this:
 ```python
-from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
+from optimum.onnxruntime.modeling_ort import ORTModelForCustomTasks
 
-model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
-embeddings = model.encode(sentences)
-print(embeddings)
+model = ORTModelForCustomTasks.from_pretrained("optimum/sbert-all-MiniLM-L6-with-pooler")
+tokenizer = AutoTokenizer.from_pretrained("optimum/sbert-all-MiniLM-L6-with-pooler")
+inputs = tokenizer("I love burritos!", return_tensors="pt")
+pred = model(**inputs)
 ```
-## Usage (HuggingFace Transformers)
-Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
+You will also be able to leverage the pipeline API in transformers:
 ```python
-from transformers import AutoTokenizer, AutoModel
-import torch
-import torch.nn.functional as F
-
-#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
-    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)
-
-# Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
-
-# Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
-model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
-
-# Tokenize sentences
-encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+from transformers import pipeline
 
-# Compute token embeddings
-with torch.no_grad():
-    model_output = model(**encoded_input)
-# Perform pooling
-sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
-# Normalize embeddings
-sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
-print("Sentence embeddings:")
-print(sentence_embeddings)
+onnx_extractor = pipeline("feature-extraction", model=model, tokenizer=tokenizer)
+text = "I love burritos!"
+pred = onnx_extractor(text)
 ```
 ## Evaluation Results
 For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-MiniLM-L6-v2)