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: 578 Bytes
299bfda f5aea10 299bfda | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | # 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)
results = clf.predict_batch([
"Спасибо, это действительно помогло.",
"I can't believe this happened again.",
"Мне тревожно перед завтрашней встречей.",
])
for row in results:
print(row["text"])
print(row["top"])
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