emo-detector

emo-detector is a multi-label emotion detection model for text. It predicts one or more emotions from the following labels:

• anger
• fear
• joy
• sadness
• surprise

Model Details

• Architecture: Pretrained DeBERTa + custom FFNN classifier
• Task: Multi-label text classification
• Tokenizer: DeBERTa tokenizer (microsoft/deberta-v3-base)
• Output: Probabilities → Thresholded to 0/1

Custom Model Class

This model uses a custom architecture defined inside the emo_detector/ module:

• emo_detector/configuration_bert_ffnn.py → BertFFNNConfig
• emo_detector/modeling_bert_ffnn.py → BERT_FFNN

To load or fine-tune this model, you must download the full repository (including the emo_detector/ folder). The recommended way is to use snapshot_download() from Hugging Face Hub.

Installation

pip install torch transformers huggingface_hub

Usage

import sys
import torch
from transformers import AutoTokenizer
from huggingface_hub import snapshot_download

# Download entire repository
repo_dir = snapshot_download("NeuralNest05/emo-detector")
sys.path.append(repo_dir)

# Import custom architecture + config
from emo_detector.configuration_bert_ffnn import BertFFNNConfig
from emo_detector.modeling_bert_ffnn import BERT_FFNN

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("NeuralNest05/emo-detector")

# Load model config and architecture
config = BertFFNNConfig.from_pretrained("NeuralNest05/emo-detector")
model = BERT_FFNN(config)

# Load weights
model.load_state_dict(torch.load(f"{repo_dir}/pytorch_model.bin", map_location=DEVICE))
model.to(DEVICE)
model.eval()

# Example prediction
texts = ["I am very happy today!", "This is scary..."]
encodings = tokenizer(texts, truncation=True, padding=True, return_tensors="pt").to(DEVICE)

with torch.no_grad():
    logits = model(**encodings)
    probs = torch.sigmoid(logits)
    threshold = 0.5
    preds = (probs > threshold).int()

print(preds)

Output Format

Each prediction corresponds to the five emotion labels in this order:

["anger", "fear", "joy", "sadness", "surprise"]

Output is a multi-hot vector, e.g.:

[0, 0, 1, 0, 0] → joy

License

MIT License

Acknowledgements

• Microsoft DeBERTa-v3
• Hugging Face Transformers
• PyTorch