
# ONNX convert all-MiniLM-L6-v2
## Conversion of [sentence-transformers/all-MiniLM-L6-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-L6-with-pooler")
tokenizer = AutoTokenizer.from_pretrained("vamsibanda/sbert-all-MiniLM-L6-with-pooler")
inputs = tokenizer("I love burritos!", return_tensors="pt")
pred = model(**inputs)
embedding = pred['pooler_output']
```
{"language": "en", "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "onnx"], "pipeline_tag": "sentence-similarity"}
sentence-transformers onnx bert feature-extraction sentence-similarity transformers en license:apache-2.0 endpoints_compatible region:us
TAGS
#sentence-transformers #onnx #bert #feature-extraction #sentence-similarity #transformers #en #license-apache-2.0 #endpoints_compatible #region-us 


# ONNX convert all-MiniLM-L6-v2
## Conversion of sentence-transformers/all-MiniLM-L6-v2
This is a sentence-transformers 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 installed:

Then you can use the model like this:

# ONNX convert all-MiniLM-L6-v2 ## Conversion of sentence-transformers/all-MiniLM-L6-v2
This is a sentence-transformers 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 installed:

Then you can use the model like this:
TAGS
#sentence-transformers #onnx #bert #feature-extraction #sentence-similarity #transformers #en #license-apache-2.0 #endpoints_compatible #region-us 
 # ONNX convert all-MiniLM-L6-v2 ## Conversion of sentence-transformers/all-MiniLM-L6-v2
This is a sentence-transformers 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 installed:

Then you can use the model like this:
passage: TAGS
#sentence-transformers #onnx #bert #feature-extraction #sentence-similarity #transformers #en #license-apache-2.0 #endpoints_compatible #region-us 
# ONNX convert all-MiniLM-L6-v2## Conversion of sentence-transformers/all-MiniLM-L6-v2
This is a sentence-transformers 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 installed:

Then you can use the model like this: