fyaronskiy/ModernBERT-large-english-go-emotions

Summary

This is ModernBERT-large model finetuned on go_emotions dataset for multilabel classification. Model can be used to extract all emotions from english text or detect certain emotions. Thresholds are selected on validation set by maximizing f1 macro over all labels. You can use Flash Attention 2 to speed up inference.

The quality of the model varies greatly across all classes (look at the table with metrics below). There are classes like admiration, amusement, optimism, fear, remorse and others where the model shows high recognition quality, and classes that pose difficulties for the model - disappointment, realization that do have much fewer examples in the training data.

Usage

Using model is easy with Huggingface Transformers.

ModernBERT architecture is supported in the transformers version 4.48.0 and later, so you need to install it:

pip install "transformers>=4.48.0"

Here is how you can extract emotions contained in text:

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained('fyaronskiy/ModernBERT-large-go-emotions')
model = AutoModelForSequenceClassification.from_pretrained('fyaronskiy/ModernBERT-large-go-emotions')
best_thresholds = [0.5510204081632653, 0.26530612244897955, 0.14285714285714285, 0.12244897959183673, 0.44897959183673464, 0.22448979591836732, 0.2040816326530612, 0.4081632653061224, 0.5306122448979591, 0.22448979591836732, 0.2857142857142857, 0.3061224489795918, 0.2040816326530612, 0.14285714285714285, 0.1020408163265306, 0.4693877551020408, 0.24489795918367346, 0.3061224489795918, 0.2040816326530612, 0.36734693877551017, 0.2857142857142857, 0.04081632653061224, 0.3061224489795918, 0.16326530612244897, 0.26530612244897955, 0.32653061224489793, 0.12244897959183673, 0.2040816326530612]
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))

def detect_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(detect_emotions('You have excellent service and the best coffee in the city, I love your coffee shop!'))
#['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('You have excellent service and the best coffee in the city, I love your coffee shop!'))
#{'admiration': 0.982, 'love': 0.689, 'approval': 0.014, 'gratitude': 0.003, 'joy': 0.003, 'amusement': 0.001, 'curiosity': 0.001, 'excitement': 0.001, 'realization': 0.001, 'surprise': 0.001, 'anger': 0.0, 'annoyance': 0.0, 'caring': 0.0, 'confusion': 0.0, 'desire': 0.0, 'disappointment': 0.0, 'disapproval': 0.0, 'disgust': 0.0, 'embarrassment': 0.0, 'fear': 0.0, 'grief': 0.0, 'nervousness': 0.0, 'optimism': 0.0, 'pride': 0.0, 'relief': 0.0, 'remorse': 0.0, 'sadness': 0.0, 'neutral': 0.0}

Eval results on test split of go-emotions

	precision	recall	f1-score	support	threshold
admiration	0.68	0.72	0.7	504	0.55
amusement	0.76	0.91	0.83	264	0.27
anger	0.44	0.53	0.48	198	0.14
annoyance	0.27	0.46	0.34	320	0.12
approval	0.41	0.38	0.4	351	0.45
caring	0.37	0.46	0.41	135	0.22
confusion	0.36	0.51	0.42	153	0.2
curiosity	0.45	0.77	0.57	284	0.41
desire	0.66	0.46	0.54	83	0.53
disappointment	0.41	0.26	0.32	151	0.22
disapproval	0.39	0.54	0.45	267	0.29
disgust	0.52	0.41	0.46	123	0.31
embarrassment	0.52	0.41	0.45	37	0.2
excitement	0.29	0.59	0.39	103	0.14
fear	0.55	0.78	0.65	78	0.1
gratitude	0.96	0.88	0.92	352	0.47
grief	0.29	0.67	0.4	6	0.24
joy	0.57	0.66	0.61	161	0.31
love	0.74	0.87	0.8	238	0.2
nervousness	0.37	0.43	0.4	23	0.37
optimism	0.6	0.58	0.59	186	0.29
pride	0.28	0.44	0.34	16	0.04
realization	0.36	0.19	0.24	145	0.31
relief	0.62	0.45	0.53	11	0.16
remorse	0.51	0.84	0.63	56	0.27
sadness	0.54	0.56	0.55	156	0.33
surprise	0.47	0.63	0.54	141	0.12
neutral	0.58	0.82	0.68	1787	0.2
micro avg	0.54	0.67	0.6	6329
macro avg	0.5	0.58	0.52	6329
weighted avg	0.55	0.67	0.6	6329
samples avg	0.59	0.69	0.61	6329