- Libraries
- ZeroModels
How to use zeromodels/multi-qa-mpnet-base-dot-v1 with ZeroModels:
# pip install -U zeromodels
# ZeroModels is pure Keras 3, so pick a backend: "jax", "torch" or "tensorflow".
import os
os.environ["KERAS_BACKEND"] = "jax"
from zeromodels import AutoZModel
# AutoZModel reads the repo's model_type and loads the matching class.
# For a task head use the matching loader, e.g. AutoZMImageClassify / AutoZMDetect /
# AutoZMSemanticSegment / AutoZMTextGenerate (see zeromodels.auto).
model = AutoZModel.from_weights("zeromodels/multi-qa-mpnet-base-dot-v1")
- Keras
How to use zeromodels/multi-qa-mpnet-base-dot-v1 with Keras:
# !pip install -U keras tensorflow huggingface_hub
# Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here;
# "jax" and "torch" also work for computation once TensorFlow is installed.
import os
os.environ["KERAS_BACKEND"] = "tensorflow"
import keras
model = keras.saving.load_model("hf://zeromodels/multi-qa-mpnet-base-dot-v1")
- sentence-transformers
How to use zeromodels/multi-qa-mpnet-base-dot-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("zeromodels/multi-qa-mpnet-base-dot-v1")
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] - Notebooks
- Google Colab
- Kaggle
