Text Classification
Transformers
Safetensors
English
Russian
xlm-roberta
emotion-classification
multi-label-classification
goemotions
english
russian
affective-computing
text-embeddings-inference
Instructions to use proxy3d/multi-motions-28 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use proxy3d/multi-motions-28 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="proxy3d/multi-motions-28")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("proxy3d/multi-motions-28") model = AutoModelForSequenceClassification.from_pretrained("proxy3d/multi-motions-28", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 671 Bytes
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message: "If you use this model or inference code, please credit Ilya Zelenskiy (proxy3d) and link to the model repository."
title: "Multi-Motions 28 — EN/RU"
type: software
authors:
- given-names: Ilya
family-names: Zelenskiy
version: 1.0.0
date-released: 2026-09-20
license: MIT
repository-code: "https://huggingface.co/proxy3d/multi-motions-28"
url: "https://t.me/greenruff"
abstract: "Inference release of a bilingual English/Russian XLM-RoBERTa multi-label emotion classifier with 28 GoEmotions-compatible outputs."
keywords:
- emotion classification
- GoEmotions
- XLM-RoBERTa
- English
- Russian
- multi-label classification
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