mandarjoshi/trivia_qa
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How to use AIFunOver/all-mpnet-base-v2-openvino-8bit with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("AIFunOver/all-mpnet-base-v2-openvino-8bit")
sentences = [
"That is a happy person",
"That is a happy dog",
"That is a very happy person",
"Today is a sunny day"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4]How to use AIFunOver/all-mpnet-base-v2-openvino-8bit with Transformers:
# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("AIFunOver/all-mpnet-base-v2-openvino-8bit")
model = AutoModelForMaskedLM.from_pretrained("AIFunOver/all-mpnet-base-v2-openvino-8bit")This model is a quantized version of sentence-transformers/all-mpnet-base-v2 and is converted to the OpenVINO format. This model was obtained via the nncf-quantization space with optimum-intel.
First make sure you have optimum-intel installed:
pip install optimum[openvino]
To load your model you can do as follows:
from optimum.intel import OVModelForFeatureExtraction
model_id = "AIFunOver/all-mpnet-base-v2-openvino-8bit"
model = OVModelForFeatureExtraction.from_pretrained(model_id)
Base model
sentence-transformers/all-mpnet-base-v2