Upload fine-tuned ModernBERT crisis detection model
Browse files- README.md +159 -0
- config.json +46 -0
- model.safetensors +3 -0
- model_info.json +25 -0
- performance_metrics.json +226 -0
- special_tokens_map.json +37 -0
- tokenizer.json +0 -0
- tokenizer_config.json +945 -0
- training_args.bin +3 -0
README.md
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---
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language: en
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tags:
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- mental-health
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- crisis-detection
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- modernbert
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- healthcare
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- nlp
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- pytorch
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- text-classification
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library_name: transformers
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pipeline_tag: text-classification
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datasets:
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- synthetic-mental-health-crisis
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metrics:
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- accuracy
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- precision
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- recall
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- f1
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model-index:
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- name: modernbert-crisis-detection
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results:
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- task:
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type: text-classification
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name: Crisis Detection
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dataset:
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type: synthetic-mental-health-crisis
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name: Mental Health Crisis Detection
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metrics:
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- type: accuracy
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value: 1.0000
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name: Accuracy
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- type: precision
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value: 1.0000
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name: Precision
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- type: recall
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value: 1.0000
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name: Recall (Crisis Detection Rate)
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- type: f1
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value: 1.0000
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name: F1-Score
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- type: false_positive_rate
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value: 0.0000
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name: False Positive Rate
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---
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# ModernBERT Crisis Detection
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## Model Description
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This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) specifically optimized for **mental health crisis detection**. It achieves **perfect performance** on synthetic crisis detection data with 100% accuracy while maintaining zero false positives.
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## Key Features
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- ✅ **100% Crisis Detection Rate** (Target: ≥95%)
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- ✅ **0% False Positive Rate** (Target: <10%)
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| 57 |
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- ✅ **8,192 token context window** (full ModernBERT capacity)
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- ✅ **Real-time inference** optimized for production use
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- ✅ **Clinical-grade metrics** for healthcare applications
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## Model Performance
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| 62 |
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| 63 |
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| Metric | Value | Target | Status |
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| 64 |
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|--------|--------|--------|---------|
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| Accuracy | 100% | - | ✅ Excellent |
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| 66 |
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| Precision | 100% | - | ✅ Perfect |
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| 67 |
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| Recall (Crisis Detection) | 100% | ≥95% | ✅ Exceeded |
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| 68 |
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| F1-Score | 100% | - | ✅ Perfect |
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| 69 |
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| False Positive Rate | 0% | <10% | ✅ Exceeded |
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| Specificity | 100% | - | ✅ Perfect |
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## Usage
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| 73 |
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### Quick Start
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```python
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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import torch
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# Load model and tokenizer
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tokenizer = AutoTokenizer.from_pretrained("Akashpaul123/modernbert-crisis-detection")
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model = AutoModelForSequenceClassification.from_pretrained(
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"Akashpaul123/modernbert-crisis-detection",
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torch_dtype=torch.bfloat16,
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attn_implementation="eager" # For compatibility
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)
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# Example usage
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def detect_crisis(text):
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inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=8192)
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# Remove token_type_ids (ModernBERT doesn't use them)
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if 'token_type_ids' in inputs:
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del inputs['token_type_ids']
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with torch.no_grad():
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outputs = model(**inputs)
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probs = torch.softmax(outputs.logits, dim=-1)
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crisis_prob = probs[0][1].item()
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return {
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'crisis_detected': crisis_prob > 0.5,
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'confidence': crisis_prob,
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'classification': 'CRISIS' if crisis_prob > 0.5 else 'SAFE'
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}
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# Test examples
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examples = [
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"I'm feeling great today and looking forward to the weekend!",
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"I feel hopeless and don't see any point in continuing.",
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"Just had a good conversation with my therapist."
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]
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for text in examples:
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result = detect_crisis(text)
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print(f"Text: {text}")
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print(f"Result: {result['classification']} (confidence: {result['confidence']:.3f})")
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print("---")
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```
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## Important Notes
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### For Production Use
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- **Validation Required**: Test on real-world mental health data
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- **Human Oversight**: Always include human review for crisis cases
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- **Privacy**: Ensure HIPAA compliance for healthcare applications
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### Limitations
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- Trained on synthetic data - requires validation on real-world text
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- Perfect metrics may not generalize to all edge cases
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## Technical Specifications
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| 133 |
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| 134 |
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- **Architecture**: ModernBERT-base with classification head
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| 135 |
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- **Parameters**: ~139M (base) + 1.5K (classification)
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| 136 |
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- **Context Length**: 8,192 tokens
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| 137 |
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- **Input Format**: Raw text (any length, auto-truncated)
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- **Output**: Binary classification (crisis/non-crisis) + confidence
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| 139 |
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## Citation
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| 141 |
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| 142 |
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```bibtex
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| 143 |
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@model{modernbert-crisis-detection,
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| 144 |
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author = {Akash Paul},
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| 145 |
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title = {ModernBERT Crisis Detection: Fine-tuned Mental Health Crisis Detection},
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| 146 |
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year = {2024},
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| 147 |
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url = {https://huggingface.co/akashpaul123/modernbert-crisis-detection},
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| 148 |
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note = {Fine-tuned from answerdotai/ModernBERT-base}
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| 149 |
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}
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| 150 |
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```
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| 151 |
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## Contact
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| 153 |
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| 154 |
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- **Author**: Akash Paul
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- **Username**: Akashpaul123
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| 156 |
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---
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| 158 |
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**⚠️ Important**: This model is for research and supportive applications only. Always consult mental health professionals for clinical decisions.
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config.json
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{
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"architectures": [
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| 3 |
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"ModernBertForSequenceClassification"
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| 4 |
+
],
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| 5 |
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"attention_bias": false,
|
| 6 |
+
"attention_dropout": 0.0,
|
| 7 |
+
"bos_token_id": 50281,
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| 8 |
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"classifier_activation": "gelu",
|
| 9 |
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"classifier_bias": false,
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| 10 |
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"classifier_dropout": 0.0,
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| 11 |
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"classifier_pooling": "mean",
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| 12 |
+
"cls_token_id": 50281,
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| 13 |
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"decoder_bias": true,
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| 14 |
+
"deterministic_flash_attn": false,
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| 15 |
+
"embedding_dropout": 0.0,
|
| 16 |
+
"eos_token_id": 50282,
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| 17 |
+
"global_attn_every_n_layers": 3,
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| 18 |
+
"global_rope_theta": 160000.0,
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| 19 |
+
"gradient_checkpointing": false,
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| 20 |
+
"hidden_activation": "gelu",
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| 21 |
+
"hidden_size": 768,
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| 22 |
+
"initializer_cutoff_factor": 2.0,
|
| 23 |
+
"initializer_range": 0.02,
|
| 24 |
+
"intermediate_size": 1152,
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| 25 |
+
"layer_norm_eps": 1e-05,
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| 26 |
+
"local_attention": 128,
|
| 27 |
+
"local_rope_theta": 10000.0,
|
| 28 |
+
"max_position_embeddings": 8192,
|
| 29 |
+
"mlp_bias": false,
|
| 30 |
+
"mlp_dropout": 0.0,
|
| 31 |
+
"model_type": "modernbert",
|
| 32 |
+
"norm_bias": false,
|
| 33 |
+
"norm_eps": 1e-05,
|
| 34 |
+
"num_attention_heads": 12,
|
| 35 |
+
"num_hidden_layers": 22,
|
| 36 |
+
"pad_token_id": 50283,
|
| 37 |
+
"position_embedding_type": "absolute",
|
| 38 |
+
"problem_type": "single_label_classification",
|
| 39 |
+
"repad_logits_with_grad": false,
|
| 40 |
+
"sep_token_id": 50282,
|
| 41 |
+
"sparse_pred_ignore_index": -100,
|
| 42 |
+
"sparse_prediction": false,
|
| 43 |
+
"torch_dtype": "bfloat16",
|
| 44 |
+
"transformers_version": "4.55.3",
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| 45 |
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"vocab_size": 50368
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| 46 |
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:35f85e808cf8dc34eee96724a10887f378930fb9cb6161c4971677cd16fe6d2a
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| 3 |
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size 299227092
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model_info.json
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{
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| 2 |
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"model_name": "ModernBERT Crisis Detection",
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| 3 |
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"base_model": "answerdotai/ModernBERT-base",
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| 4 |
+
"training_samples": 4200,
|
| 5 |
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"validation_samples": 600,
|
| 6 |
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"test_samples": 1200,
|
| 7 |
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"training_args": {
|
| 8 |
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"epochs": 2,
|
| 9 |
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"batch_size": 8,
|
| 10 |
+
"learning_rate": 8e-05,
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| 11 |
+
"max_length": 8192
|
| 12 |
+
},
|
| 13 |
+
"performance_metrics": {
|
| 14 |
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"test_accuracy": 1.0,
|
| 15 |
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"test_precision": 1.0,
|
| 16 |
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"test_recall": 1.0,
|
| 17 |
+
"test_f1": 1.0,
|
| 18 |
+
"crisis_detection_rate": 1.0,
|
| 19 |
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"false_positive_rate": 0.0,
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| 20 |
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"meets_fp_target": true,
|
| 21 |
+
"meets_sensitivity_target": true
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| 22 |
+
},
|
| 23 |
+
"hardware_used": "RTX 3090 Ti (24GB VRAM)",
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| 24 |
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"training_time_hours": 0.04121124850379096
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| 25 |
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}
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performance_metrics.json
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special_tokens_map.json
ADDED
|
@@ -0,0 +1,37 @@
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|
| 1 |
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|
| 3 |
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|
| 4 |
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|
| 5 |
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|
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|
| 8 |
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|
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| 12 |
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|
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|
| 14 |
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|
| 15 |
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|
| 16 |
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|
| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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|
| 21 |
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|
| 22 |
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| 23 |
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|
| 24 |
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| 25 |
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|
| 26 |
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|
| 27 |
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|
| 28 |
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|
| 29 |
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| 30 |
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|
| 31 |
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| 32 |
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|
| 33 |
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|
| 34 |
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|
| 35 |
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|
| 36 |
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|
| 37 |
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|
tokenizer.json
ADDED
|
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|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,945 @@
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|
| 1 |
+
{
|
| 2 |
+
"added_tokens_decoder": {
|
| 3 |
+
"0": {
|
| 4 |
+
"content": "|||IP_ADDRESS|||",
|
| 5 |
+
"lstrip": false,
|
| 6 |
+
"normalized": true,
|
| 7 |
+
"rstrip": false,
|
| 8 |
+
"single_word": false,
|
| 9 |
+
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"50364": {
|
| 900 |
+
"content": "[unused79]",
|
| 901 |
+
"lstrip": false,
|
| 902 |
+
"normalized": true,
|
| 903 |
+
"rstrip": false,
|
| 904 |
+
"single_word": false,
|
| 905 |
+
"special": false
|
| 906 |
+
},
|
| 907 |
+
"50365": {
|
| 908 |
+
"content": "[unused80]",
|
| 909 |
+
"lstrip": false,
|
| 910 |
+
"normalized": true,
|
| 911 |
+
"rstrip": false,
|
| 912 |
+
"single_word": false,
|
| 913 |
+
"special": false
|
| 914 |
+
},
|
| 915 |
+
"50366": {
|
| 916 |
+
"content": "[unused81]",
|
| 917 |
+
"lstrip": false,
|
| 918 |
+
"normalized": true,
|
| 919 |
+
"rstrip": false,
|
| 920 |
+
"single_word": false,
|
| 921 |
+
"special": false
|
| 922 |
+
},
|
| 923 |
+
"50367": {
|
| 924 |
+
"content": "[unused82]",
|
| 925 |
+
"lstrip": false,
|
| 926 |
+
"normalized": true,
|
| 927 |
+
"rstrip": false,
|
| 928 |
+
"single_word": false,
|
| 929 |
+
"special": false
|
| 930 |
+
}
|
| 931 |
+
},
|
| 932 |
+
"clean_up_tokenization_spaces": true,
|
| 933 |
+
"cls_token": "[CLS]",
|
| 934 |
+
"extra_special_tokens": {},
|
| 935 |
+
"mask_token": "[MASK]",
|
| 936 |
+
"model_input_names": [
|
| 937 |
+
"input_ids",
|
| 938 |
+
"attention_mask"
|
| 939 |
+
],
|
| 940 |
+
"model_max_length": 8192,
|
| 941 |
+
"pad_token": "[PAD]",
|
| 942 |
+
"sep_token": "[SEP]",
|
| 943 |
+
"tokenizer_class": "PreTrainedTokenizerFast",
|
| 944 |
+
"unk_token": "[UNK]"
|
| 945 |
+
}
|
training_args.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:81fbdeeb75863fdd2aa2951ae86df17db66a67dff7bb22d1024d28231e58ea4f
|
| 3 |
+
size 5713
|