metadata

library_name: transformers
license: mit
datasets:
  - seara/ru_go_emotions
base_model: ai-forever/ruRoberta-large
language:
  - ru
tags:
  - Text Classification
  - emotion-classification
  - emotion-recognition
  - emotion-detection
  - emotion
  - multilabel
metrics:
  - f1
  - precision
  - recall

This is ruRoberta-large model finetuned on ru_go_emotions dataset for multilabel classification. Model can be used to extract all emotions from text or detect certain emotions. Thresholds are selected on validation set by maximizing f1 macro over all labels.

Usage

Using model with Huggingface Transformers:

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("fyaronskiy/ruRoberta-large-ru-go-emotions")
model = AutoModelForSequenceClassification.from_pretrained("fyaronskiy/ruRoberta-large-ru-go-emotions")

best_thresholds = [0.36734693877551017, 0.2857142857142857, 0.2857142857142857, 0.16326530612244897, 0.14285714285714285, 0.14285714285714285, 0.18367346938775508, 0.3469387755102041, 0.32653061224489793, 0.22448979591836732, 0.2040816326530612, 0.2857142857142857, 0.18367346938775508, 0.2857142857142857, 0.24489795918367346, 0.7142857142857142, 0.02040816326530612, 0.3061224489795918, 0.44897959183673464, 0.061224489795918366, 0.18367346938775508, 0.04081632653061224, 0.08163265306122448, 0.1020408163265306, 0.22448979591836732, 0.3877551020408163, 0.3469387755102041, 0.24489795918367346]
LABELS = ['admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral']
ID2LABEL = dict(enumerate(LABELS))

Here is how you can extract emotions contained in text:

def predict_emotions(text):
  inputs = tokenizer(text, truncation=True, add_special_tokens=True, max_length=128, return_tensors='pt')
  with torch.no_grad():
      logits = model(**inputs).logits
  probas = torch.sigmoid(logits).squeeze(dim=0)  
  class_binary_labels = (probas > torch.tensor(best_thresholds)).int()
  return [ID2LABEL[label_id] for label_id, value in enumerate(class_binary_labels) if value == 1]

print(predict_emotions('У вас отличный сервис и лучший кофе в городе, обожаю вашу кофейню!'))

#['admiration', 'love']

This is the way to get all emotions and their scores:

def predict(text):
    inputs = tokenizer(text, truncation=True, add_special_tokens=True, max_length=128, return_tensors='pt')
    with torch.no_grad():
        logits = model(**inputs).logits
    probas = torch.sigmoid(logits).squeeze(dim=0).tolist()
    probas = [round(proba, 3) for proba in probas]    
    
    labels2probas = dict(zip(LABELS, probas))
    probas_dict_sorted = dict(sorted(labels2probas.items(), key=lambda x: x[1], reverse=True))
    return probas_dict_sorted

print(predict('У вас отличный сервис и лучший кофе в городе, обожаю вашу кофейню!'))
'''{'admiration': 0.81,
 'love': 0.538,
 'joy': 0.041,
 'gratitude': 0.031,
 'approval': 0.026,
 'excitement': 0.023,
 'neutral': 0.009,
 'curiosity': 0.006,
 'amusement': 0.005,
 'desire': 0.005,
 'realization': 0.005,
 'caring': 0.004,
 'confusion': 0.004,
 'surprise': 0.004,
 'disappointment': 0.003,
 'disapproval': 0.003,
 'anger': 0.002,
 'annoyance': 0.002,
 'disgust': 0.002,
 'fear': 0.002,
 'grief': 0.002,
 'optimism': 0.002,
 'pride': 0.002,
 'relief': 0.002,
 'sadness': 0.002,
 'embarrassment': 0.001,
 'nervousness': 0.001,
 'remorse': 0.001}
'''

Eval results on test split of ru-go-emotions

	precision	recall	f1-score	support	threshold
admiration	0.63	0.75	0.69	504	0.37
amusement	0.76	0.91	0.83	264	0.29
anger	0.47	0.32	0.38	198	0.29
annoyance	0.33	0.39	0.36	320	0.16
approval	0.27	0.58	0.37	351	0.14
caring	0.32	0.59	0.41	135	0.14
confusion	0.41	0.52	0.46	153	0.18
curiosity	0.45	0.73	0.55	284	0.35
desire	0.54	0.31	0.40	83	0.33
disappointment	0.31	0.34	0.33	151	0.22
disapproval	0.31	0.57	0.40	267	0.20
disgust	0.44	0.40	0.42	123	0.29
embarrassment	0.48	0.38	0.42	37	0.18
excitement	0.29	0.43	0.34	103	0.29
fear	0.56	0.78	0.65	78	0.24
gratitude	0.95	0.85	0.89	352	0.71
grief	0.03	0.33	0.05	6	0.02
joy	0.48	0.58	0.53	161	0.31
love	0.73	0.84	0.78	238	0.45
nervousness	0.24	0.48	0.32	23	0.06
optimism	0.57	0.54	0.56	186	0.18
pride	0.67	0.38	0.48	16	0.04
realization	0.18	0.31	0.23	145	0.08
relief	0.30	0.27	0.29	11	0.10
remorse	0.53	0.84	0.65	56	0.22
sadness	0.56	0.53	0.55	156	0.39
surprise	0.55	0.57	0.56	141	0.35
neutral	0.59	0.79	0.68	1787	0.24
micro avg	0.50	0.66	0.57	6329
macro avg	0.46	0.55	0.48	6329
weighted avg	0.53	0.66	0.58	6329