Add Emoticare

Add EmotiCare: DistilBERT multi-label emotion classifier (GoEmotions, 28 classes)

.gitattributes

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README.md

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 ---
 license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+  - text-classification
+  - multi-label-classification
+  - emotion-detection
+  - distilbert
+  - pytorch
+datasets:
+  - go_emotions
+language:
+  - en
+metrics:
+  - f1
+pipeline_tag: text-classification
 ---
+
+# EmotiCare — Multi-Label Emotion Classifier
+
+EmotiCare is a fine-tuned [DistilBERT](https://huggingface.co/distilbert-base-uncased) model for **multi-label emotion detection** in English text. Given a sentence, it predicts one or more emotions from 28 categories drawn from the [GoEmotions](https://huggingface.co/datasets/go_emotions) dataset.
+
+It is designed for use in applications that need nuanced, fine-grained emotion understanding — such as mental health tools, sentiment dashboards, chatbots, and content moderation systems.
+
+## Emotions
+
+The model classifies text into 28 emotions:
+
+`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`
+
+## Model Details
+
+| Property | Value |
+|---|---|
+| Base model | `distilbert-base-uncased` |
+| Architecture | DistilBertForSequenceClassification |
+| Task | Multi-label text classification |
+| Dataset | GoEmotions (simplified, 43,410 train samples) |
+| Training epochs | 3 |
+| Max sequence length | 512 tokens |
+| Framework | PyTorch + 🤗 Transformers |
+
+## Evaluation Results
+
+Evaluated on the GoEmotions test set (5,427 examples):
+
+| Metric | Score |
+|---|---|
+| F1 Macro | **0.4019** |
+| F1 Micro | **0.5702** |
+| Eval Loss | 0.0843 |
+
+> Note: Multi-label emotion classification on GoEmotions is a challenging task due to class imbalance and overlapping emotions. F1 Micro of ~0.57 is competitive with similar fine-tuned DistilBERT baselines.
+
+## Inference
+
+### Using the 🤗 `pipeline` (recommended)
+
+```python
+from transformers import pipeline
+import torch
+
+classifier = pipeline(
+    "text-classification",
+    model="YOUR_USERNAME/emoticare",  # replace with your HF repo path
+    tokenizer="YOUR_USERNAME/emoticare",
+    top_k=None,                        # return scores for all labels
+    device=0 if torch.cuda.is_available() else -1,
+)
+
+text = "I can't believe how thoughtful that was, I'm so touched."
+results = classifier(text)
+
+# Filter to emotions above a confidence threshold
+threshold = 0.3
+detected = [r for r in results[0] if r["score"] > threshold]
+for emotion in sorted(detected, key=lambda x: -x["score"]):
+    print(f"{emotion['label']:<20} {emotion['score']:.3f}")
+```
+
+**Example output:**
+```
+gratitude            0.847
+admiration           0.612
+love                 0.431
+```
+
+---
+
+### Manual inference (more control)
+
+```python
+import torch
+import torch.nn.functional as F
+from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
+
+model_name = "YOUR_USERNAME/emoticare"  # replace with your HF repo path
+
+tokenizer = DistilBertTokenizer.from_pretrained(model_name)
+model = DistilBertForSequenceClassification.from_pretrained(model_name)
+model.eval()
+
+def predict_emotions(text: str, threshold: float = 0.3):
+    inputs = tokenizer(
+        text,
+        return_tensors="pt",
+        truncation=True,
+        max_length=512,
+        padding=True,
+    )
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    probs = torch.sigmoid(logits).squeeze()  # sigmoid for multi-label
+
+    emotions = model.config.id2label
+    results = [
+        {"label": emotions[i], "score": float(probs[i])}
+        for i in range(len(emotions))
+        if float(probs[i]) > threshold
+    ]
+    return sorted(results, key=lambda x: -x["score"])
+
+# Example
+print(predict_emotions("I'm so proud of everything we've built together!"))
+```
+
+---
+
+### Batch inference
+
+```python
+texts = [
+    "I'm terrified of what might happen next.",
+    "This is the best day of my life!",
+    "I don't really feel anything about it.",
+]
+
+inputs = tokenizer(
+    texts,
+    return_tensors="pt",
+    truncation=True,
+    max_length=512,
+    padding=True,
+)
+
+with torch.no_grad():
+    logits = model(**inputs).logits
+
+probs = torch.sigmoid(logits)  # shape: (batch_size, 28)
+threshold = 0.3
+
+for i, text in enumerate(texts):
+    detected = [
+        model.config.id2label[j]
+        for j in range(28)
+        if probs[i][j] > threshold
+    ]
+    print(f"Text: {text}")
+    print(f"Emotions: {', '.join(detected) or 'none above threshold'}\n")
+```
+
+## Training Details
+
+- **Base model:** `distilbert-base-uncased`
+- **Dataset:** [go_emotions](https://huggingface.co/datasets/go_emotions) (simplified config)
+- **Loss function:** Binary Cross-Entropy (multi-label)
+- **Optimizer:** AdamW with linear warmup + decay
+- **Learning rate:** 2e-5 (peak)
+- **Batch size:** 16
+- **Epochs:** 3
+- **Best checkpoint:** step 8142 (epoch 3)
+
+## Limitations
+
+- Trained on Reddit comments — performance may degrade on formal text, non-native English, or very short inputs.
+- Some rare emotions (grief, pride, relief) have limited training examples and lower per-class F1.
+- Outputs are probabilities; the optimal threshold (default 0.3) may need tuning for your use case.
+
+## Citation
+
+If you use this model, please cite the GoEmotions dataset:
+
+```bibtex
+@inproceedings{demszky-etal-2020-goemotions,
+  title     = {{GoEmotions}: A Dataset of Fine-Grained Emotions},
+  author    = {Demszky, Dorottya and Movshovitz-Attias, Dana and Ko, Jeongwook
+               and Cowen, Alan and Nemade, Gaurav and Ravi, Sujith},
+  booktitle = {Proceedings of the 58th Annual Meeting of the Association for
+               Computational Linguistics},
+  year      = {2020},
+}
+```

config.json

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+{
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "dtype": "float32",
+  "hidden_dim": 3072,
+  "id2label": {
+    "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"
+  },
+  "label2id": {
+    "admiration": 0,
+    "amusement": 1,
+    "anger": 2,
+    "annoyance": 3,
+    "approval": 4,
+    "caring": 5,
+    "confusion": 6,
+    "curiosity": 7,
+    "desire": 8,
+    "disappointment": 9,
+    "disapproval": 10,
+    "disgust": 11,
+    "embarrassment": 12,
+    "excitement": 13,
+    "fear": 14,
+    "gratitude": 15,
+    "grief": 16,
+    "joy": 17,
+    "love": 18,
+    "nervousness": 19,
+    "optimism": 20,
+    "pride": 21,
+    "realization": 22,
+    "relief": 23,
+    "remorse": 24,
+    "sadness": 25,
+    "surprise": 26,
+    "neutral": 27
+  },
+  "initializer_range": 0.02,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "problem_type": "multi_label_classification",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "transformers_version": "4.57.3",
+  "vocab_size": 30522
+}

model.safetensors

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+size 202334208

special_tokens_map.json

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tokenizer_config.json

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+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
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+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}