Instructions to use Jarbas/ovos-model2vec-intents-potion-8M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use Jarbas/ovos-model2vec-intents-potion-8M with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("Jarbas/ovos-model2vec-intents-potion-8M") - sentence-transformers
How to use Jarbas/ovos-model2vec-intents-potion-8M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Jarbas/ovos-model2vec-intents-potion-8M") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Deprecated. This model is superseded and is kept only so existing installs keep resolving. It was trained before the OVOS intent corpus was rebuilt from a pinned, reproducible pipeline, and its label set no longer matches what the OVOS m2v pipeline registers.
Use OpenVoiceOS/ovos-m2v-intents-multilingual instead, or OpenVoiceOS/ovos-m2v-intents-en on an English-only device. Both ship a
labels.jsonbeside the weights.
model_en_potion-base-8M Model Card
This Model2Vec model is a fine-tuned version of the potion-base-8M Model2Vec model. It also includes a classifier head on top.
Installation
Install model2vec using pip:
pip install model2vec[inference]
Usage
Load this model using the from_pretrained method:
from model2vec.inference import StaticModelPipeline
# Load a pretrained Model2Vec model
model = StaticModelPipeline.from_pretrained("model_en_potion-base-8M")
# Predict labels
predicted = model.predict(["Example sentence"])
Additional Resources
Library Authors
Model2Vec was developed by the Minish Lab team consisting of Stephan Tulkens and Thomas van Dongen.
Citation
Please cite the Model2Vec repository if you use this model in your work.
@article{minishlab2024model2vec,
author = {Tulkens, Stephan and {van Dongen}, Thomas},
title = {Model2Vec: Fast State-of-the-Art Static Embeddings},
year = {2024},
url = {https://github.com/MinishLab/model2vec}
}
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minishlab/potion-base-8M