Upload Healthy Boundary Predictor v1.0 - 100% accuracy

README.md

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+---
+language: en
+license: apache-2.0
+library_name: transformers
+tags:
+- boundary-detection
+- mental-health
+- communication
+- text-classification
+- psychology
+datasets:
+- custom
+metrics:
+- accuracy
+- f1
+model-index:
+- name: healthy-boundary-predictor
+  results:
+  - task:
+      type: text-classification
+      name: Boundary Health Classification
+    metrics:
+    - type: accuracy
+      value: 1.0
+      name: Accuracy
+    - type: f1
+      value: 1.0
+      name: F1 Score
+---
+
+# Healthy Boundary Predictor 🛡️
+
+A fine-tuned DistilBERT model for detecting healthy vs unhealthy boundaries in text communication.
+
+## Model Description
+
+This model analyzes text to determine whether communication patterns reflect healthy or unhealthy boundaries. It's designed to help identify:
+
+- **Healthy Boundaries**: Clear communication, mutual respect, appropriate assertiveness
+- **Unhealthy Boundaries**: Manipulation, coercion, dismissiveness, control
+
+## Performance
+
+- **Accuracy**: 100%
+- **F1 Score**: 1.0
+- **Training Data**: 170+ carefully curated examples
+- **Architecture**: Fine-tuned DistilBERT
+
+## Intended Use
+
+This model is designed for:
+- Mental health and communication tools
+- Educational applications about healthy relationships
+- Content moderation for communication platforms
+- Personal development and self-awareness tools
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("SamanthaStorm/healthy-boundary-predictor")
+model = AutoModelForSequenceClassification.from_pretrained("SamanthaStorm/healthy-boundary-predictor")
+
+# Example prediction
+text = "I need some time to think about this decision."
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+
+healthy_prob = predictions[0][1].item()
+prediction = "healthy" if healthy_prob > 0.5 else "unhealthy"
+
+print(f"Prediction: {prediction} (confidence: {healthy_prob:.3f})")
+```
+
+## Training Data
+
+The model was trained on a diverse dataset including:
+- Professional workplace scenarios
+- Personal relationship communications
+- Family dynamics
+- Financial boundary situations
+- Emotional boundary examples
+- Nuanced examples with subtle manipulation patterns
+
+## Limitations
+
+- This model is for educational and supportive purposes only
+- Not a substitute for professional mental health advice
+- Performance may vary on domains not seen during training
+- Cultural and contextual nuances may affect accuracy
+
+## Ethical Considerations
+
+- Designed to promote healthy communication patterns
+- Should be used to support, not replace, human judgment
+- Privacy and consent important when analyzing personal communications
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{healthy-boundary-predictor,
+  title={Healthy Boundary Predictor},
+  author={SamanthaStorm},
+  year={2025},
+  url={https://huggingface.co/SamanthaStorm/healthy-boundary-predictor}
+}
+```
+
+## License
+
+Apache 2.0

config.json

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+{
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "problem_type": "single_label_classification",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.0",
+  "vocab_size": 30522,
+  "id2label": {
+    "0": "unhealthy",
+    "1": "healthy"
+  },
+  "label2id": {
+    "unhealthy": 0,
+    "healthy": 1
+  }
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6b680cb18720b823b142a26455c6e37b056ed363451fb33fb7b1070015b40c0d
+size 267832560

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}