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")# 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
| { | |
| "author": { | |
| "name": "Ilya Zelenskiy", | |
| "alias": "proxy3d", | |
| "telegram": "https://t.me/greenruff" | |
| }, | |
| "project_links": { | |
| "communication_styles_article": "https://iproxy3d.github.io/communication-styles-llm/", | |
| "communication_styles_github": "https://github.com/iproxy3d/communication-styles-llm" | |
| }, | |
| "evaluation_protocol": "3 independent seeds; native-RU benchmarks were not used for training, checkpoint selection, or threshold tuning", | |
| "goemotions_test": { | |
| "en": { | |
| "micro_f1_mean": 0.595278, | |
| "micro_f1_std": 0.001725, | |
| "macro_f1_mean": 0.519635, | |
| "macro_f1_std": 0.00347, | |
| "micro_ap_mean": 0.63659, | |
| "micro_ap_std": 0.00412, | |
| "macro_ap_mean": 0.506498, | |
| "macro_ap_std": 0.002801 | |
| }, | |
| "ru_translated": { | |
| "micro_f1_mean": 0.565428, | |
| "micro_f1_std": 0.001362, | |
| "macro_f1_mean": 0.482554, | |
| "macro_f1_std": 0.005028, | |
| "micro_ap_mean": 0.594971, | |
| "micro_ap_std": 0.002251, | |
| "macro_ap_mean": 0.46094, | |
| "macro_ap_std": 0.005664 | |
| } | |
| }, | |
| "native_ru": { | |
| "cedr": { | |
| "n": 1882, | |
| "labels": [ | |
| "joy", | |
| "sadness", | |
| "surprise", | |
| "fear", | |
| "anger" | |
| ], | |
| "micro_f1_mean": 0.517982, | |
| "micro_f1_std": 0.013487, | |
| "macro_f1_mean": 0.529451, | |
| "macro_f1_std": 0.018099, | |
| "micro_ap_mean": 0.638756, | |
| "micro_ap_std": 0.000341, | |
| "macro_ap_mean": 0.676202, | |
| "macro_ap_std": 0.007899 | |
| }, | |
| "semeval2025_ru": { | |
| "n": 1000, | |
| "labels": [ | |
| "anger", | |
| "disgust", | |
| "fear", | |
| "joy", | |
| "sadness", | |
| "surprise" | |
| ], | |
| "micro_f1_mean": 0.63545, | |
| "micro_f1_std": 0.014176, | |
| "macro_f1_mean": 0.633188, | |
| "macro_f1_std": 0.017979, | |
| "micro_ap_mean": 0.840718, | |
| "micro_ap_std": 0.001325, | |
| "macro_ap_mean": 0.844767, | |
| "macro_ap_std": 0.004708 | |
| } | |
| } | |
| } | |