ovos-model2vec-intents-distiluse-base-multilingual-cased-v2/README.md

base_model, library_name, license, model_name, tags, language, datasets

base_model	library_name	license	model_name	tags	language	datasets
Jarbas/m2v-256-distiluse-base-multilingual-cased-v2	model2vec	mit	ovos-model2vec-intents-distiluse-base-multilingual-cased-v2	embeddings static-embeddings sentence-transformers	en de it pt da ca gl fr es nl eu	Jarbas/ovos-llm-augmented-intents Jarbas/ovos-weather-intents Jarbas/music_queries_templates Jarbas/OVOSGitLocalize-Intents Jarbas/ovos_intent_examples Jarbas/ovos-common-query-intents

model_mul_m2v-256-distiluse-base-multilingual-cased-v2 Model Card

This Model2Vec model is a fine-tuned version of the distiluse-base-multilingual-cased-v2 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_mul_m2v-256-distiluse-base-multilingual-cased-v2")

# Predict labels
predicted = model.predict(["Example sentence"])

Additional Resources

• Model2Vec Repo
• Model2Vec Base Models
• Model2Vec Results
• Model2Vec Tutorials
• Website

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}
}