Add model with Git LFS support

README.md

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 ---
 license: mit
+tags:
+- truthfulness
+- bert
+- text-classification
+- dual-classifier
+pipeline_tag: text-classification
+---
+
+# Truthfulness Detection Model
+
+Fine-tuned BERT model for detecting truthfulness in text at both token and sentence levels.
+
+## Model Description
+
+This model uses a dual-classifier architecture on top of BERT to:
+- Classify truthfulness at the sentence level (returns probability 0-1)
+- Classify truthfulness for each token (returns probability 0-1 per token)
+
+Low scores indicate likely false statements, high scores indicate likely true statements.
+
+## Example Output
+
+For "The earth is flat.":
+- Sentence score: 0.0736 (7.36% - correctly identified as false)
+- Token scores: ~0.10 for each token
+
+## Training
+
+- Base model: bert-base-uncased  
+- Training samples: 6,330
+- Epochs: 3
+- Batch size: 16
+- Training time: 49 seconds on H100
+
+## Custom Architecture Required
+
+⚠️ This model uses a custom `BERTForDualTruthfulness` class. You cannot load it with standard AutoModel.
+See the [implementation code](https://huggingface.co/prompterminal/classifier/blob/main/model_architecture.py) for the model class definition.---
+license: mit
 ---

config.json

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+{
+  "architectures": ["BERTForDualTruthfulness"],
+  "model_type": "bert",
+  "hidden_size": 768,
+  "num_labels": 2,
+  "vocab_size": 30522,
+  "hidden_dropout_prob": 0.1,
+  "attention_probs_dropout_prob": 0.1,
+  "max_position_embeddings": 512,
+  "type_vocab_size": 2,
+  "initializer_range": 0.02,
+  "layer_norm_eps": 1e-12,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "intermediate_size": 3072,
+  "_name_or_path": "bert-base-uncased"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d60413c7b238b1536b5b1cb96f5394cf1e1bde5182362545999f1b95395e8863
+size 437965064