Senti is a lightweight emotion classification model fine-tuned on the Google GoEmotions dataset.
It uses MobileBERT, making it small and efficient enough to run on low-powered devices such as smartphones, Raspberry Pi, or edge AI systems.
This repo contains Training Jupyter notebook and infer.py for simple inference.
| ID | Emotion | ID | Emotion | ID | Emotion | ID | Emotion |
|---|---|---|---|---|---|---|---|
| 0 | admiration | 1 | amusement | 2 | anger | 3 | annoyance |
| 4 | approval | 5 | caring | 6 | confusion | 7 | curiosity |
| 8 | desire | 9 | disappointment | 10 | disapproval | 11 | disgust |
| 12 | embarrassment | 13 | excitement | 14 | fear | 15 | gratitude |
| 16 | grief | 17 | joy | 18 | love | 19 | nervousness |
| 20 | optimism | 21 | pride | 22 | realization | 23 | relief |
| 24 | remorse | 25 | sadness | 26 | surprise | 27 | neutral |
- 📦 Compact — based on MobileBERT (optimized for efficiency and speed).
- 💬 Emotion-rich — trained on 27 emotion labels + neutral from the GoEmotions dataset.
- ⚡ Low-resource friendly — designed to work on edge devices and mobile apps.
- 🔌 Plug-and-play — easy integration with Hugging Face
transformers.
Here are the evaluation results of Senti on the GoEmotions validation set:
| Metric | Value |
|---|---|
| Loss | 0.085 |
| F1-score | 0.586 |
| ROC AUC | 0.752 |
| Accuracy | 0.460 |
- Source: Google GoEmotions
- Classes: 27 emotions + neutral
- Task type: Single-label classification (best label per input text)
There are multiple ways to use this model in Huggingface Transformers. Possibly the simplest is using a pipeline:
pip install torch transformersfrom transformers import pipeline
classifier = pipeline(task="text-classification", model="AR04/Senti", top_k=None)
sentences = ["hi! you are looking beautiful today dear"]
model_outputs = classifier(sentences)
print(model_outputs[0])
# produces a list of dicts for each of the labelsOutput:
[{'label': 'admiration', 'score': 0.9517803192138672}, {'label': 'love', 'score': 0.18317067623138428}, {'label': 'joy', 'score': 0.03131399303674698}, {'label': 'neutral', 'score': 0.01567094214260578}, {'label': 'surprise', 'score': 0.009232419542968273}, {'label': 'approval', 'score': 0.007308646105229855}, {'label': 'excitement', 'score': 0.006345656234771013}, {'label': 'pride', 'score': 0.004945244640111923}, {'label': 'caring', 'score': 0.0038624939043074846}, {'label': 'realization', 'score': 0.0023580112028867006}, {'label': 'desire', 'score': 0.0017759536858648062}, {'label': 'optimism', 'score': 0.0013220690889284015}, {'label': 'sadness', 'score': 0.001188945840112865}, {'label': 'disappointment', 'score': 0.0009136834414675832}, {'label': 'gratitude', 'score': 0.0008250900427810848}, {'label': 'relief', 'score': 0.0005154621903784573}, {'label': 'amusement', 'score': 0.0004376845608931035}, {'label': 'fear', 'score': 0.00038696840056218207}, {'label': 'embarrassment', 'score': 0.0003084330528508872}, {'label': 'grief', 'score': 0.00019462488126009703}, {'label': 'confusion', 'score': 0.00018893269589170814}, {'label': 'annoyance', 'score': 0.0001587819424457848}, {'label': 'curiosity', 'score': 0.0001355114800389856}, {'label': 'remorse', 'score': 0.00011744408402591944}, {'label': 'anger', 'score': 0.00010586195276118815}, {'label': 'disgust', 'score': 9.386352030560374e-05}, {'label': 'nervousness', 'score': 7.547048153355718e-05}, {'label': 'disapproval', 'score': 3.7117086321813986e-05}]