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README.md

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+---
+base_model: microsoft/deberta-v3-base
+library_name: peft
+pipeline_tag: text-classification
+language:
+- en
+tags:
+- misinformation-detection
+- social-media
+- fakett
+- peft
+- lora
+- transformers
+- base_model:adapter:microsoft/deberta-v3-base
+---
+
+# DeBERTa — Text-Only Misinformation Detection on FakeTT
+
+**Developed by Andrei-Gabriel Radu.**
+
+Bachelor thesis supervised by **Ciprian-Octavian Truică and Elena-Simona Apostol**,  
+**National University of Science and Technology POLITEHNICA Bucharest**.
+
+LoRA adapter fine-tuned from `microsoft/deberta-v3-base` for binary **text-only misinformation classification** on the FakeTT social-media video dataset.
+
+This model accompanies the bachelor thesis *Misinformation Detection in Social Media Videos*.
+
+
+## Results
+
+| Dataset | Modality | Macro-F1 |
+|---|---|---:|
+| FakeTT | Text-only | 0.7776 |
+
+## Model
+
+- **Base model:** `microsoft/deberta-v3-base`
+- **Task:** Binary misinformation classification
+- **Modality:** Text-only
+- **Fine-tuning:** LoRA / PEFT
+- **Dataset:** FakeTT
+- **Number of classes:** 2
+- **Primary metric:** Macro-F1
+
+## Training
+
+- **LoRA rank (`r`):** 8
+- **LoRA alpha:** 32
+- **LoRA dropout:** 0.05
+- **Target modules:** `key_proj`, `query_proj`, `value_proj`, `dense`
+- **Bias:** none
+
+## Usage
+
+```python
+import torch
+from peft import PeftModel
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+repo_id = "DS4AI-UPB/deberta-misinfo-lora"
+base_model_id = "microsoft/deberta-v3-base"
+
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+base_model = AutoModelForSequenceClassification.from_pretrained(base_model_id, num_labels=2)
+model = PeftModel.from_pretrained(base_model, repo_id).eval()
+
+text = "Example social media video description."
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+
+with torch.no_grad():
+    logits = model(**inputs).logits
+
+print(logits.argmax(dim=-1).item())
+```
+
+> Use the class-to-label mapping from the original FakeTT training pipeline.
+
+## Intended Use
+
+Research and benchmarking of English-language text-only misinformation detection for social-media video content.
+
+## Limitations
+
+This is a classification model, not a factual verification system. It cannot inspect the associated video and can degrade under domain shift.
+
+## Citation
+
+```bibtex
+@thesis{radu2026misinformation,
+  title  = {Misinformation Detection in Social Media Videos},
+  author = {Radu, Andrei-Gabriel},
+  school = {National University of Science and Technology POLITEHNICA Bucharest},
+  year   = {2026}
+}
+```
+

adapter_config.json

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+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "microsoft/deberta-v3-base",
+  "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": 32,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": [
+    "classifier",
+    "score"
+  ],
+  "peft_type": "LORA",
+  "peft_version": "0.18.1",
+  "qalora_group_size": 16,
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "key_proj",
+    "query_proj",
+    "value_proj",
+    "dense"
+  ],
+  "target_parameters": null,
+  "task_type": "SEQ_CLS",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e43abe4d7f255a475f76e0e56df972604d13f4c37e48ffc2c0c53c1fc076d77e
+size 5384856

tokenizer_config.json

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+{
+  "add_prefix_space": true,
+  "backend": "tokenizers",
+  "bos_token": "[CLS]",
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "eos_token": "[SEP]",
+  "extra_special_tokens": [
+    "[PAD]",
+    "[CLS]",
+    "[SEP]"
+  ],
+  "is_local": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "split_by_punct": false,
+  "tokenizer_class": "DebertaV2Tokenizer",
+  "unk_id": 3,
+  "unk_token": "[UNK]",
+  "vocab_type": "spm"
+}