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
File size: 609 Bytes
299bfda f5aea10 299bfda | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | # Author: Ilya Zelenskiy (proxy3d)
# Telegram channel: https://t.me/greenruff
# Communication Styles LLM: https://iproxy3d.github.io/communication-styles-llm/
from goemotions_en_ru import EmotionClassifier
MODEL_ID = "proxy3d/multi-motions-28"
clf = EmotionClassifier(MODEL_ID)
for text in [
"I finally finished it, what a relief!",
"Я наконец закончил — какое облегчение!",
]:
result = clf.predict(text, top_k=5)
print(text)
for item in result["top"]:
print(f" {item['label']:14s} {item['score']:.3f}")
print("active:", result["labels"])
|