| --- |
| language: en |
| license: mit |
| tags: |
| - text-classification |
| - multi-label |
| - emotion-detection |
| - huggingface |
| --- |
| |
| # 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 |
| ```bash |
| pip install torch transformers huggingface_hub |
| ``` |
|
|
| ## Usage |
|
|
| ```python |
| 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 |
