readme: ---
  pipeline_tag: sentence-similarity
  language: en
  license: apache-2.0
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  - onnx
  ---
  
  # ONNX convert all-MiniLM-L12-v2
  ## Conversion of [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2)
  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:
  ```
  python -m pip install optimum
  ```
  Then you can use the model like this:
  ```python
  from optimum.onnxruntime.modeling_ort import ORTModelForCustomTasks
  
  model = ORTModelForCustomTasks.from_pretrained("vamsibanda/sbert-all-MiniLM-L12-with-pooler")
  tokenizer = AutoTokenizer.from_pretrained("vamsibanda/sbert-all-MiniLM-L12-with-pooler")
  inputs = tokenizer("I love burritos!", return_tensors="pt")
  pred = model(**inputs)
  embedding = pred['pooler_output']
  ```