tanaos-emotion-detection-v1: A small but performant emotion detection model
This model was created by Tanaos with the Artifex Python library.
This is an emotion detection model based on microsoft/Multilingual-MiniLM-L12-H384 and fine-tuned on a synthetic dataset to classify the main emotion expressed in any text input into one of the following categories:
| Label | Description |
|---|---|
joy | Text that expresses happiness or positivity |
anger | Text that expresses anger or frustration |
fear | Text that expresses fear or anxiety |
sadness | Text that expresses sadness or sorrow |
surprise | Text that expresses surprise or shock |
disgust | Text that expresses disgust or revulsion |
excitement | Text that expresses excitement or enthusiasm |
neutral | Text that either expresses a neutral or indifferent emotion, or that does not express any emotion at all on account of being purely factual or descriptive |
neutral is the default label for text that is either factual or does not express a clear emotion.
These categories were chosen to cover a wide range of common emotions in text, in order to create a versatile and general-purpose emotion detection model, applicable across various industries and use cases.
This model can be used to analyze customer feedback, social media posts, reviews, and other text data to gain insights into the emotional tone and sentiment expressed by users.
How to Use
Use this model through the Artifex library:
install Artifex with
pip install artifex
use the model with
from artifex import Artifex
emotion_detection = Artifex().emotion_detection()
print(emotion_detection("I can't wait for the concert tonight!"))
# >>> [{'label': 'excitement', 'score': 0.9941}]
Model Description
- Base model:
microsoft/Multilingual-MiniLM-L12-H384 - Task: Text classification (emotion detection)
- Languages: English
- Fine-tuning data: A synthetic, custom dataset of 10,000 utterances, each belonging to one of 12 different emotion categories.
Training Details
This model was trained using the Artifex Python library
pip install artifex
by providing the following instructions and generating 10,000 synthetic training samples:
from artifex import Artifex
ed = Artifex().emotion_detection()
ed.train(
domain="general",
classes={
"joy": "text that expresses happiness or positivity",
"anger": "text that expresses anger or frustration",
"fear": "text that expresses fear or anxiety",
"sadness": "text that expresses sadness or sorrow",
"surprise": "text that expresses surprise or shock",
"disgust": "text that expresses disgust or revulsion",
"excitement": "text that expresses excitement or enthusiasm",
"neutral": "text that either expresses a neutral or indifferent emotion, or that does not express any emotion at all on account of being purely factual or descriptive."
},
num_samples=10000
)
Intended Uses
This model is intended to:
- Classify the emotional tone of text inputs across multiple languages.
- Be used in applications such as customer feedback analysis, social media monitoring, and sentiment analysis.
- Serve as a lightweight alternative to larger models for emotion detection tasks.
Not intended for:
- Highly specialized domains where emotions differ significantly from the predefined categories.