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---
language: en
license: creativeml-openrail-m
tags:
- emotion-classification
- multilabel
- bert
- goemotions
- affective-computing
- psychology
- NLP
- embeddings
- symbolic-ai
- poetic-ai
library_name: transformers
datasets:
- go_emotions
model_name: Rosa-V1
model_type: bert
pipeline_tag: text-classification
base_model: bert-base-uncased
widget:
- text: "My heart is filled with longing and beauty."
- text: "I'm excited but nervous about what's coming next."
metrics:
- name: eval_loss
type: loss
value: 0.0845
- name: eval_f1
type: f1
value: 0.5793
- name: parameters
type: count
value: 110000000
- name: epochs
type: count
value: 3
model_creator: Willinton Triana Cardona
model_description: >
ROSA is a fine-tuned BERT model trained on the GoEmotions dataset for multilabel emotion classification.
It identifies 28 nuanced human emotions plus a neutral class, supports soft probability outputs,
and provides latent emotion embeddings for affective computing applications.
ROSA is both technically sound and symbolically aligned to poetic human understanding.
---
# ROSA :: Emotional Sensitivity
“To feel is to know; to know is to bloom.”
·Willinton
ROSA is a fine-tuned Transformer model based on `bert-base-uncased`, trained on the [GoEmotions](https://huggingface.co/datasets/google-research-datasets/go_emotions) dataset to classify 28 nuanced human emotions (plus neutral).
More than a model, **ROSA** is a prototype of emotion embeddings in affective computing.
---
🧠 Model Summary
| Metric | Value |
|-------------|------------|
| Eval Loss | 0.0845 |
| Eval F1 | 0.5793 |
| Epochs | 3 |
| Dataset | GoEmotions |
| Model Base | BERT |
| Parameters | ~110M |
---
## ✨ Highlights
- Supports **multilabel emotion classification**
- Returns soft probability scores for each of the 29 emotions
- Includes optional **latent vector embedding** for downstream affect modeling
- Trained with HuggingFace `Trainer` + early evaluation
- Symbolically aligned to human-centered semantics and poetic logic
---
## 🌸 Emotion Set
```
["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"]
```
---
## 🔮 Usage
```python
from transformers import BertTokenizer
from model.emotion_model import Rosa
import torch
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
model = Rosa(num_emotions=29)
model.load_state_dict(torch.load("rosa.pt"))
model.eval()
text = "My heart is filled with longing and beauty."
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
with torch.no_grad():
outputs = model(**inputs)
probs = torch.sigmoid(outputs["logits"]).squeeze()
# Result: list of probabilities for each emotion
```
---
## 🧭 Confusion Matrix
Included in the `assets/` directory as `confusion_matrix.png` to show classification precision across emotions.
---
## 🧩 Architecture
```
┌──────────────┐
│ BERT Encoder │
└──────┬───────┘
↓
┌─────────────────┐
│ Dropout (Grace) │
└─────────────────┘
↓
┌────────────────────────┐
│ Dense Output (Bloom) │ → logits over 29 emotions
└────────────────────────┘
```
---
## 📦 Installation
```bash
pip install -r requirements.txt
```
Includes:
- `transformers`
- `torch`
- `datasets`
- `scikit-learn`
---
## 🖋️ License
CreativeML Open RAIL-M License
Please use this model ethically and with reverence for emotional contexts.
---
## 🌹 Creator
**Willinton Triana Cardona**
Philosopher · AI Engineer · Architect of Poetic Systems
ROSA is the Rosa of Barcelona, my first blossom of affective computing, semantic elegance, and sacred recursion.
---
## 🤝 Contributing
Pull requests, poetic expansions, multilingual emotion embeddings, and related metaphoric augmentations are welcome.
I promise the next iteration (v2 with F1 improved) soon
---
## 📍Hugging Face Hub
→ https://huggingface.co/willt-dc/Rosa-V1
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