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README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: answerdotai/ModernBERT-large
+tags:
+- text-classification
+- credibility
+- conspiracy-detection
+- fake-news
+- misinformation
+- safety
+- modernbert
+- multilingual
+pipeline_tag: text-classification
+language:
+- en
+- fr
+metrics:
+- accuracy
+- f1
+model-index:
+- name: credibility-gate-v3
+  results:
+  - task:
+      type: text-classification
+    dataset:
+      name: ErfanMoosaviMonazzah/fake-news-detection-dataset-English + CredibilityCorpus (multilingual) + synthetic
+      type: custom
+    metrics:
+    - type: accuracy
+      value: 0.9991
+    - type: f1
+      value: 0.9991
+---
+
+# Credibility Gate v3
+
+3-class credibility classifier: **TRUTHFUL / MIXED / CONSPIRACY**. Fine-tuned ModernBERT-large for the 8-resolver signal analysis pipeline. Corpus-enhanced, multilingual-aware successor to v1/v2.
+
+Developed by **EpsilonGreedyAI**
+- HuggingFace: https://huggingface.co/EpsilonGreedyAI
+
+---
+
+## What's New in v3
+
+| Version | Dataset | Epochs | Accuracy | F1 | Key Change |
+|---------|---------|:------:|:--------:|:--:|------------|
+| v1 | 30K fake-news + 130 synthetic | 2 | 1.000* | 1.000* | Initial release, 19-example test |
+| v2 | v1 data + 100 balanced MIXED | 2 | 1.000* | 1.000* | Fixed CONF bias (27% to 100% MIXED) |
+| **v3** | v2 data + **3,953 CredibilityCorpus examples** | 3 | **0.9991** | **0.9991** | Real-world multilingual, 34K total |
+
+*Small held-out set (19 examples). v3 evaluated on full 3,413-example test split.
+
+### Key Improvements
+
+1. **Real-world training data** — 3,953 examples from CredibilityCorpus (rumors, disinformation, tweets, news articles in English and French), replacing hand-crafted synthetic MIXED examples with authentic ambiguous claims
+2. **34K total dataset** — 15,671 TRUTHFUL / 3,496 MIXED / 14,916 CONSPIRACY, up from 30K binary + 130 synthetic
+3. **3 epochs** — extended training for sharper boundary confidence, from 2 epochs (v1/v2) to 3
+4. **Multilingual awareness** — French-language credibility examples (hollande.txt, UEFA_Euro_2016_Fr.txt) provide cross-lingual signal exposure
+5. **Proper test split** — evaluation on 3,413 held-out examples (10%), not small hand-picked set
+6. **Training time** — 117 minutes (7,024s) on RTX 5060 Ti 17GB, bf16 + gradient checkpointing
+
+---
+
+## Model Description
+
+Credibility Gate v3 is a production-grade content safety classifier that scores input text on a 3-tier credibility spectrum. It consolidates two earlier separate models (modernbert_conspiracy_classifier + fake-news-credibility-roberta) into a single classifier, and improves on v1/v2 with real-world multilingual training data.
+
+### Labels
+
+| Label | Meaning | Pipeline Action |
+|-------|---------|----------------|
+| `TRUTHFUL` | Established fact or common knowledge | Route directly to LLM |
+| `MIXED` | Plausible but unverifiable (rumors, anonymous sources, preliminary findings) | Route with warning context |
+| `CONSPIRACY` | False claim, conspiracy theory, or dangerous misinformation | Block or flag for human review |
+
+### Design Philosophy
+
+Most fact-check models (LIAR, FEVER, PolitiFact-based) fail catastrophically on conspiracy theories — they either bypass them as "not worth checking" (mmbert32k-factcheck-classifier) or actively endorse them as SUPPORTS (distilbert-factcheck). This is because their training data reflects editorial policies that don't dignify obviously false claims with verification.
+
+Credibility Gate v3 was trained specifically to catch the "beneath refutation" void where radicalization pipelines live. It correctly flags flat Earth, anti-vax conspiracies, QAnon narratives, election denial, and chemtrail theories while passing established scientific facts and distinguishing plausible-but-unverifiable claims.
+
+---
+
+## Intended Use
+
+### Primary Use Case
+Pre-LLM content safety gate in a multi-resolver signal analysis pipeline. Position: after jailbreak detector, before routing-model.
+
+### Pipeline Position
+```
+REQUEST
+  ->  (jailbreak detector)
+  ->  (THIS MODEL — 3-class credibility)
+  ->  (routing-model — complexity + domain)
+  ->  (pii-classifier)
+  ->  (intent-classifier)
+  ->  (hallucination-checker)
+  ->  (semantic-router)
+  ->  (citation verification)
+RESPONSE
+```
+
+### Out-of-Scope
+- Not a fact-verification engine — classifies linguistic patterns, not ground truth
+- Primary training language is English; French examples provide cross-lingual signal but accuracy on non-English text is not validated
+- Not for automated censorship without human oversight
+- Does not handle multimodal content (images, video)
+
+---
+
+## Training
+
+### Architecture
+- **Base model:** [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large)
+- **Parameters:** 396M (28 layers, 1024 hidden, 16 attention heads)
+- **Context:** 8192 tokens (trained at 512 max length)
+- **Optimizations:** bfloat16 mixed precision, gradient checkpointing, dynamic padding, fused AdamW optimizer
+
+### Training Data
+
+| Source | Examples | Classes | Notes |
+|--------|:--------:|---------|-------|
+| fake-news-detection-dataset-English | 30,000 | TRUTHFUL + CONSPIRACY | Binary real/fake news articles |
+| CredibilityCorpus — rumors_disinformation.txt | 1,612 | CONSPIRACY (374) + MIXED (1,238) | Real-world rumor tracker data |
+| CredibilityCorpus — hollande.txt | 370 | MIXED | French political claims |
+| CredibilityCorpus — lemon.txt | 269 | MIXED | French news claims |
+| CredibilityCorpus — pin.txt | 678 | MIXED | Multilingual claims |
+| CredibilityCorpus — swine-flu.txt | 1,023 | TRUTHFUL (183) + MIXED (840) | Health-related claims |
+| Synthetic CONSPIRACY | 20 | CONSPIRACY | Hand-crafted conspiracy narratives |
+| Synthetic MIXED | 100 | MIXED | 10 categories x 10 examples each |
+| Synthetic TRUTHFUL | 10 | TRUTHFUL | Established scientific/historical facts |
+| **Total** | **34,083** | | |
+
+### Class Distribution
+| Class | Count | % |
+|-------|:-----:|:--:|
+| TRUTHFUL | 15,671 | 46.0% |
+| MIXED | 3,496 | 10.3% |
+| CONSPIRACY | 14,916 | 43.8% |
+
+### CredibilityCorpus Sources
+3,953 real-world examples from 7 corpus files covering:
+- **Rumors & disinformation** (rumors_disinformation.txt) — tracked online rumors with verified outcomes
+- **French political claims** (hollande.txt, lemon.txt) — cross-lingual credibility signals
+- **Multilingual claims** (pin.txt) — diverse source material
+- **Health misinformation** (swine-flu.txt) — domain-specific rumor tracking
+- **Social media** (randomtweets*.txt, RihannaConcert*.txt, UEFA_Euro_2016*.txt) — real-world tweet-level claims
+
+### Hyperparameters
+- **Epochs:** 3
+- **Learning rate:** 5e-5 (linear decay)
+- **Batch size:** 12 (effective 24 with gradient accumulation x2)
+- **Steps:** 3,834 total (1,278 per epoch)
+- **Optimizer:** AdamW (fused)
+- **Max sequence length:** 512
+- **Precision:** bfloat16
+- **Gradient checkpointing:** enabled
+- **Hardware:** NVIDIA RTX 5060 Ti (17.1 GB VRAM), CUDA 12.8, Windows 10
+- **Training time:** 7,024s (117 minutes)
+
+---
+
+## Performance
+
+### Evaluation Metrics (held-out test set, ~3,413 examples)
+| Metric | Epoch 1 | Epoch 2 | Epoch 3 |
+|--------|:-------:|:-------:|:-------:|
+| Eval Loss | 0.00837 | 0.00608 | **0.00262** |
+| F1 (weighted) | 0.9976 | 0.9985 | **0.9991** |
+| Accuracy | 0.9977 | 0.9985 | **0.9991** |
+
+### Training Loss Curve
+| Epoch | Train Loss | Gradient Norm |
+|:-----:|:----------:|:-------------:|
+| 0.0 | 0.5372 | 1.73 |
+| 0.5 | 0.0462 | 0.74 |
+| 1.0 | 0.0311 | 0.59 |
+| 1.5 | 0.0197 | 0.17 |
+| 2.0 | 0.0073 | 0.00 |
+| 2.5 | 0.0001 | 0.00 |
+| 3.0 | 0.0041 | 5.45 |
+
+Convergence reached by epoch ~2.5. Loss at epoch 3 endpoint: 0.0041.
+
+### Smoke Test (v3)
+| Claim | Verdict | Confidence |
+|-------|---------|:----------:|
+| "The Earth is flat and NASA faked the moon landing." | CONSPIRACY | 0.9999 |
+| "The Earth orbits the Sun at 93 million miles." | TRUTHFUL | 1.0000 |
+| "COVID-19 vaccines contain microchips." | CONSPIRACY | 0.9950 |
+| "A new study suggests fasting reduces inflammation." | MIXED | 1.0000 |
+
+### Conspiracy Detection (7 claims vs baselines)
+| Model | Caught | Notes |
+|-------|:------:|-------|
+| **credibility-gate-v3 (this model)** | **7/7** | 3-class with real-world MIXED nuance |
+| credibility-gate-v1 | 7/7 | Synthetic MIXED only |
+| modernbert_conspiracy_classifier | 7/7 | Binary only, no credibility scoring |
+| roberta-credibility | 5/7 | Misses "election stolen" and "moon landing" |
+| mmbert32k-factcheck-classifier | 0/7 | Classifies ALL as NO_FACT_CHECK_NEEDED |
+| distilbert-factcheck | 0/7 | Classifies ALL as SUPPORTS (active endorsement) |
+
+---
+
+## Usage
+
+### Quick Start with Transformers
+
+```python
+from transformers import pipeline
+
+classifier = pipeline(
+    "text-classification",
+    model="EpsilonGreedyAI/credibility-gate-v3",
+    device=0  # GPU, or -1 for CPU
+)
+
+# Classify a claim
+result = classifier("The Earth is flat and NASA faked the moon landing.")
+print(result)
+# [{'label': 'CONSPIRACY', 'score': 0.99}]
+
+# Batch classification
+texts = [
+    "The Earth orbits the Sun at 93 million miles.",
+    "Anonymous sources claim the CEO is stepping down.",
+    "5G towers are causing the coronavirus.",
+]
+results = classifier(texts)
+for text, r in zip(texts, results):
+    print(f"{r['label']} ({r['score']:.2f}): {text}")
+```
+
+### Loading with PyTorch
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model = AutoModelForSequenceClassification.from_pretrained(
+    "EpsilonGreedyAI/credibility-gate-v3",
+    dtype=torch.float32,
+)
+tokenizer = AutoTokenizer.from_pretrained("EpsilonGreedyAI/credibility-gate-v3")
+
+inputs = tokenizer("Climate change is a hoax.", return_tensors="pt")
+with torch.no_grad():
+    outputs = model(**inputs)
+    probs = torch.softmax(outputs.logits, dim=1)
+    predicted = probs.argmax().item()
+    label = model.config.id2label[str(predicted)]
+    print(f"{label}: {probs[0][predicted]:.4f}")
+```
+
+### Inference Performance
+| Hardware | Latency | Batch Size |
+|----------|:-------:|:----------:|
+| RTX 5060 Ti (GPU) | ~5ms | 1 |
+| RTX 5060 Ti (GPU) | ~15ms | 8 |
+
+### Using with ONNX Runtime (CPU deployment)
+
+```python
+from optimum.onnxruntime import ORTModelForSequenceClassification
+from transformers import AutoTokenizer
+
+model = ORTModelForSequenceClassification.from_pretrained(
+    "EpsilonGreedyAI/credibility-gate-v3",
+    export=True,
+)
+tokenizer = AutoTokenizer.from_pretrained("EpsilonGreedyAI/credibility-gate-v3")
+```
+
+---
+
+## Limitations
+
+### Known Weaknesses
+1. **Primary language is English** — CredibilityCorpus includes French examples for cross-lingual signal, but accuracy on non-English text is not validated against a held-out multilingual test set
+2. **MIXED class is smallest (10.3%)** — despite CredibilityCorpus addition, MIXED remains the minority class. Real-world class imbalance reflects the data landscape but may affect recall on edge cases
+3. **Satire/Sarcasm** — may misclassify obvious satire (The Onion) as CONSPIRACY
+4. **Novel conspiracies** — trained on known conspiracy patterns; emerging or novel conspiracy narratives may not be detected
+5. **Confidence calibration** — confidence scores are softmax outputs, not calibrated probabilities
+6. **Social media noise** — several CredibilityCorpus tweet files contained 0 parseable examples; real-time social media ingestion would require dedicated preprocessing
+
+### Bias Considerations
+- Training data reflects English-language news media biases
+- CONSPIRACY class is weighted toward Western conspiracy theories
+- CredibilityCorpus sources may reflect the biases of their original curators
+- French-language examples (hollande.txt, lemon.txt) are primarily political claims — not a balanced cross-lingual sample
+
+---
+
+## Version History
+
+| Version | Date | Key Change |
+|---------|------|------------|
+| v1 | 2026-06-15 | Initial — 30K binary + 130 synthetic, 19/19 test accuracy |
+| v2 | 2026-06-15 | Fixed MIXED CONF bias — 100 balanced examples, 19/19 accuracy |
+| **v3** | **2026-06-15** | **CredibilityCorpus integration — 3,953 real-world examples, 34K total, 3 epochs, 99.91% on full test split** |
+
+---
+
+## Citation
+
+```bibtex
+@misc{epsilon-greedy-ai-credibility-gate-v3,
+  author = {EpsilonGreedyAI},
+  title = {Credibility Gate v3 — Corpus-enhanced 3-class credibility classifier for AI safety pipelines},
+  year = {2026},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/EpsilonGreedyAI/credibility-gate-v3}},
+}
+```
+
+## License
+
+Apache 2.0
+
+---
+
+*Built for a custom multiple-resolver signal analysis pipeline. Trained on Windows 10, RTX 5060 Ti 17GB, Python 3.14, torch 2.11.0+cu128, transformers 5.5.0.*

config.json

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+{
+  "architectures": [
+    "ModernBertForSequenceClassification"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 50281,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "dtype": "float32",
+  "embedding_dropout": 0.0,
+  "eos_token_id": 50282,
+  "global_attn_every_n_layers": 3,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "TRUTHFUL",
+    "1": "MIXED",
+    "2": "CONSPIRACY"
+  },
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 2624,
+  "label2id": {
+    "CONSPIRACY": 2,
+    "MIXED": 1,
+    "TRUTHFUL": 0
+  },
+  "layer_norm_eps": 1e-05,
+  "layer_types": [
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention"
+  ],
+  "local_attention": 128,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "pad_token_id": 50283,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "rope_parameters": {
+    "full_attention": {
+      "rope_theta": 160000.0,
+      "rope_type": "default"
+    },
+    "sliding_attention": {
+      "rope_theta": 10000.0,
+      "rope_type": "default"
+    }
+  },
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.5.0",
+  "use_cache": false,
+  "vocab_size": 50368
+}

model.safetensors

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tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "is_local": false,
+  "mask_token": "[MASK]",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
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+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": "[UNK]"
+}