---
base_model:
- Jarbas/m2v-256-roberta-large-bne
library_name: model2vec
license: mit
model_name: ovos-intents-es-b32e30-n1d128-m2v-256-roberta-large-bne
tags:
- embeddings
- static-embeddings
- sentence-transformers
datasets:
- Jarbas/ovos-intents-train-latest
language:
- es
---

> **Deprecated.** This model is superseded and is kept only so existing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](https://huggingface.co/OpenVoiceOS/ovos-m2v-intents-multilingual)
> instead, or [OpenVoiceOS/ovos-m2v-intents-en](https://huggingface.co/OpenVoiceOS/ovos-m2v-intents-en)
> on an English-only device. Both ship a `labels.json` beside the weights.

# ovos-intents-es-b32e30-n1d128-m2v-256-roberta-large-bne Model Card

This [Model2Vec](https://github.com/MinishLab/model2vec) model is a fine-tuned version of the [unknown](https://huggingface.co/unknown) 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:
```python
from model2vec.inference import StaticModelPipeline

# Load a pretrained Model2Vec model
model = StaticModelPipeline.from_pretrained("ovos-intents-es-b32e30-n1d128-m2v-256-roberta-large-bne")

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

## Additional Resources

- [Model2Vec Repo](https://github.com/MinishLab/model2vec)
- [Model2Vec Base Models](https://huggingface.co/collections/minishlab/model2vec-base-models-66fd9dd9b7c3b3c0f25ca90e)
- [Model2Vec Results](https://github.com/MinishLab/model2vec/tree/main/results)
- [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials)
- [Website](https://minishlab.github.io/)

## Library Authors

Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled).

## Citation

Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) 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}
}
```