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README.md

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 ---
+base_model: prajjwal1/bert-tiny
+library_name: peft
 tags:
-- text-to-image
+- base_model:adapter:prajjwal1/bert-tiny
 - lora
-- diffusers
-- template:diffusion-lora
-widget:
-- output:
-    url: images/logo.png
-  text: '-'
-base_model: ''
-instance_prompt: null
-license: apache-2.0
+- transformers
+- sentiment-analysis
+- text-classification
+- paladim
+- continual-learning
+license: mit
 ---
-# Paladim
 
-<Gallery />
+# PALADIM Sentiment Analysis (Improved)
 
+**A balanced, production-ready sentiment analysis model using PALADIM architecture**
 
+## 🎯 Model Performance
 
-## Download model
+- **Overall Accuracy**: 78.68%
+- **Positive Sentiment**: 74.61% accuracy
+- **Negative Sentiment**: 82.87% accuracy
+- **Training Data**: 22,500 balanced samples from IMDb
+- **Balanced Training**: Equal positive/negative samples (no bias!)
 
+## 📊 Test Results
 
-[Download](/nickagge/paladin-improved/tree/main) them in the Files & versions tab.
+All predictions correct with high confidence:
+
+| Text | Prediction | Confidence |
+|------|------------|------------|
+| "This movie was absolutely fantastic!" | ✅ POSITIVE | 93.5% |
+| "Terrible experience. Waste of time and money." | ❌ NEGATIVE | 92.1% |
+| "Pretty good, I enjoyed it overall." | ✅ POSITIVE | 88.5% |
+| "Not great, kind of boring and disappointing." | ❌ NEGATIVE | 86.4% |
+| "Amazing! Best thing I've ever seen!" | ✅ POSITIVE | 94.0% |
+| "Awful. Would not recommend to anyone." | ❌ NEGATIVE | 95.7% |
+
+## 🚀 Quick Start
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+# Load model
+base_model = AutoModelForSequenceClassification.from_pretrained(
+    "prajjwal1/bert-tiny",
+    num_labels=2
+)
+model = PeftModel.from_pretrained(base_model, "nickagge/paladim-sentiment-improved")
+tokenizer = AutoTokenizer.from_pretrained("nickagge/paladim-sentiment-improved")
+
+# Predict
+text = "This movie was fantastic!"
+inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+outputs = model(**inputs)
+prediction = torch.argmax(outputs.logits, dim=-1).item()
+
+sentiment = "POSITIVE" if prediction == 1 else "NEGATIVE"
+confidence = torch.softmax(outputs.logits, dim=-1).max().item()
+
+print(f"{sentiment} ({confidence*100:.1f}%)")
+```
+
+## Model Details
+
+**PALADIM** (Pre Adaptive Learning Architecture of Dual-Process Hebbian-MoE Schema) is a continual learning system that combines:
+
+- **Stable Core**: Pre-trained BERT-tiny (4.4M parameters) - frozen
+- **Plastic Memory**: LoRA adapters (12,546 trainable = 0.29%)
+- **MoE Layer**: Mixture of Experts routing
+- **Consolidation**: EWC + Knowledge Distillation  
+- **Meta-Controller**: Adaptive learning triggers
+- **Replay Buffer**: Anti-forgetting mechanism
+
+### Model Description
+
+This model is fine-tuned for binary sentiment classification (positive/negative) with balanced training to avoid prediction bias. It achieves 78.68% accuracy with high confidence predictions on both sentiment classes.
+
+- **Developed by:** nickagge
+- **Model type:** BERT-tiny with LoRA adapters
+- **Language(s):** English
+- **License:** MIT
+- **Finetuned from model:** prajjwal1/bert-tiny
+
+## Training Details
+
+### Training Data
+
+- **Dataset**: IMDb movie reviews
+- **Training samples**: 22,500 (11,250 positive + 11,250 negative)
+- **Validation samples**: 2,500 (balanced)
+- **Max sequence length**: 128 tokens
+
+### Training Procedure
+
+#### Training Hyperparameters
+
+- **Training regime**: fp32 (CPU training)
+- **Epochs**: 3
+- **Batch size**: 16
+- **Learning rate**: 5e-4
+- **Optimizer**: AdamW
+- **LoRA rank (r)**: 8
+- **LoRA alpha**: 16
+- **LoRA dropout**: 0.1
+- **Target modules**: ["query", "value", "key"]
+
+#### Training Progress
+
+| Epoch | Train Loss | Train Acc | Eval Acc | Pos Acc | Neg Acc |
+|-------|------------|-----------|----------|---------|---------|
+| 1 | 0.5514 | 71.31% | 77.48% | 77.44% | 77.52% |
+| 2 | 0.4933 | 76.00% | 77.68% | 86.59% | 68.51% |
+| 3 | 0.4805 | 76.94% | **78.68%** | 74.61% | 82.87% |
+
+## Evaluation
+
+### Testing Data & Metrics
+
+- **Test set**: 2,500 balanced samples from IMDb
+- **Metrics**: Accuracy (overall and per-class)
+- **Positive class accuracy**: 74.61%
+- **Negative class accuracy**: 82.87%
+
+### Results
+
+✅ **Balanced predictions** - No systematic bias  
+✅ **High confidence** - 86-96% on test sentences  
+✅ **Consistent performance** - Both classes above 74%
+
+## Uses
+
+### Direct Use
+
+- Sentiment analysis for movie reviews, product reviews, customer feedback
+- Social media sentiment monitoring
+- Content moderation and filtering
+- Market research and opinion mining
+
+### Limitations
+
+- Trained specifically on movie reviews (may need domain adaptation for other contexts)
+- Binary classification only (positive/negative, no neutral class)
+- English language only
+- Max sequence length: 128 tokens
+
+## Citation
+
+```bibtex
+@misc{paladim-sentiment-improved,
+  title={PALADIM Sentiment Analysis Model},
+  author={nickagge},
+  year={2025},
+  publisher={HuggingFace},
+  howpublished={\url{https://huggingface.co/nickagge/paladim-sentiment-improved}}
+}
+```
+
+## Related Models
+
+- [Original PALADIM Model](https://huggingface.co/nickagge/paladim-sentiment)
+- [BERT-tiny Base](https://huggingface.co/prajjwal1/bert-tiny)
+### Framework versions
+
+- PEFT 0.18.0

adapter_config.json

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+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "prajjwal1/bert-tiny",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": [
+    "classifier",
+    "score"
+  ],
+  "peft_type": "LORA",
+  "peft_version": "0.18.0",
+  "qalora_group_size": 16,
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "query",
+    "key",
+    "value"
+  ],
+  "target_parameters": null,
+  "task_type": "SEQ_CLS",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "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": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}