🏆 Upload elite fake news model - 99.98% accuracy!

README.md

@@ -1,3 +1,135 @@
----
-license: mit
----
+---
+language: en
+license: mit
+tags:
+- fake-news-detection
+- deberta-v3-large
+- text-classification
+- binary-classification
+- news-classification
+datasets:
+- mrisdal/fake-news
+- jainpooja/fake-news-detection
+- clmentbisaillon/fake-and-real-news-dataset
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+widget:
+- text: "Scientists announce breakthrough discovery of alien life on Mars!"
+  example_title: "Suspicious Claim"
+- text: "The Federal Reserve announced a 0.25% interest rate increase following their monthly meeting."
+  example_title: "Financial News"
+model-index:
+- name: Arko007/fact-check1-v1
+  results:
+  - task:
+      type: text-classification
+      name: Fake News Detection
+    metrics:
+    - type: accuracy
+      value: 99.98
+      name: Validation Accuracy
+    - type: f1
+      value: 99.98
+      name: Validation F1-Score
+---
+# 🏆 Elite Fake News Detection Model
+
+## Model Description
+This is a **state-of-the-art** fake news detection model based on **DeBERTa-v3-large**, achieving **99.98% accuracy** on validation data. The model was fine-tuned on a carefully curated and deduplicated dataset combining multiple high-quality fake news datasets, totaling **51,319 samples** after preprocessing.
+
+## 🚀 Performance Highlights
+- **Validation Accuracy**: 99.98%
+- **Test Accuracy**: 99.94%
+- **F1-Score**: 99.98%
+- **Precision**: 99.97%
+- **Recall**: 100.00%
+
+## Model Architecture
+- **Base Model**: microsoft/deberta-v3-large
+- **Task**: Binary Text Classification (Real vs Fake News)
+- **Parameters**: ~400M parameters
+- **Training Hardware**: NVIDIA A100-SXM4-80GB
+
+## Training Details
+- **Training Steps**: 640
+- **Batch Size**: 64
+- **Learning Rate**: 3e-05
+- **Max Length**: 512 tokens
+- **Training Time**: 0.43 hours
+- **Gradient Checkpointing**: Non-reentrant (memory optimized)
+
+## Dataset Information
+**Total Samples**: 51,319
+- **Training**: 41,055 samples
+- **Validation**: 5,132 samples
+- **Test**: 5,132 samples
+- **Fake News**: 30,123 samples
+- **Real News**: 21,196 samples
+**Source Datasets**:
+- `mrisdal/fake-news`
+- `jainpooja/fake-news-detection`
+- `clmentbisaillon/fake-and-real-news-dataset`
+
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model_name = "Arko007/fact-check1-v1"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Example prediction function
+def predict_fake_news(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+        prediction = torch.argmax(probabilities, dim=-1).item()
+
+    labels = {0: "REAL", 1: "FAKE"}
+    confidence = probabilities[0][prediction].item()
+
+    return {
+        "prediction": labels[prediction],
+        "confidence": confidence,
+        "probabilities": {
+            "REAL": probabilities[0][0].item(),
+            "FAKE": probabilities[0][1].item()
+        }
+    }
+
+# Test the model
+text = "Breaking: Scientists discover new planet in our solar system!"
+result = predict_fake_news(text)
+print(f"Prediction: {result['prediction']} ({result['confidence']:.2%} confidence)")
+```
+## Model Performance
+
+This model achieves **research-grade performance** on fake news detection, with near-perfect accuracy across all metrics. The high precision and recall indicate excellent balance between catching fake news while avoiding false positives on real news.
+
+## Limitations and Bias
+
+- Trained primarily on English news articles
+- Performance may vary on news domains not represented in training data
+- May reflect biases present in the source datasets
+- Designed for binary classification (fake vs real) only
+
+## Citation
+```bibtex
+@misc{fake-news-deberta-2025,
+author = {Arko007},
+title = {Elite Fake News Detection with DeBERTa-v3-Large},
+year = {2025},
+publisher = {Hugging Face},
+url = {[https://huggingface.co/](https://huggingface.co/)Arko007/fact-check1-v1}
+}
+```
+## License
+MIT License - Feel free to use this model for research and applications.
+---
+**Built with ❤️ using A100 80GB + DeBERTa-v3-Large**

added_tokens.json

@@ -0,0 +1,3 @@
+{
+  "[MASK]": 128000
+}

config.json

@@ -0,0 +1,45 @@
+{
+  "architectures": [
+    "DebertaV2ForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 1,
+  "dtype": "bfloat16",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "REAL",
+    "1": "FAKE"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "FAKE": 1,
+    "REAL": 0
+  },
+  "layer_norm_eps": 1e-07,
+  "legacy": true,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "norm_rel_ebd": "layer_norm",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 1024,
+  "pos_att_type": [
+    "p2c",
+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "share_att_key": true,
+  "transformers_version": "4.56.1",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f279ccdbc7c41c13c5f725bc47b013dea682c4e85991ce12a2d79ebf204a5fba
+size 870176748

special_tokens_map.json

@@ -0,0 +1,15 @@
+{
+  "bos_token": "[CLS]",
+  "cls_token": "[CLS]",
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

spm.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c679fbf93643d19aab7ee10c0b99e460bdbc02fedf34b92b05af343b4af586fd
+size 2464616

tokenizer_config.json

@@ -0,0 +1,59 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128000": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "[CLS]",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "eos_token": "[SEP]",
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "sp_model_kwargs": {},
+  "split_by_punct": false,
+  "tokenizer_class": "DebertaV2Tokenizer",
+  "unk_token": "[UNK]",
+  "vocab_type": "spm"
+}

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:300ec60d973fb27d6b612f762ce97a8e6c6122ae69a54b62e74235626f8a2dc4
+size 5841

training_summary.json

@@ -0,0 +1,44 @@
+{
+    "model_checkpoint": "microsoft/deberta-v3-large",
+    "final_metrics": {
+        "validation": {
+            "eval_loss": 0.0013762748567387462,
+            "eval_accuracy": 0.9998051441932969,
+            "eval_precision": 0.9996682149966821,
+            "eval_recall": 1.0,
+            "eval_f1": 0.9998340799734527,
+            "eval_runtime": 34.6469,
+            "eval_samples_per_second": 148.123,
+            "eval_steps_per_second": 1.183,
+            "epoch": 0.9968847352024922
+        },
+        "test": {
+            "eval_loss": 0.0015322713879868388,
+            "eval_accuracy": 0.9994154325798909,
+            "eval_precision": 0.9993362097577165,
+            "eval_recall": 0.999667994687915,
+            "eval_f1": 0.9995020746887967,
+            "eval_runtime": 34.9714,
+            "eval_samples_per_second": 146.748,
+            "eval_steps_per_second": 1.172,
+            "epoch": 0.9968847352024922
+        }
+    },
+    "training_config": {
+        "max_steps": 640,
+        "batch_size": 64,
+        "gradient_accumulation_steps": 2,
+        "learning_rate": 3e-05,
+        "max_length": 512,
+        "gradient_checkpointing": "non-reentrant"
+    },
+    "dataset_stats": {
+        "total_examples": 51319,
+        "train_size": 41055,
+        "val_size": 5132,
+        "test_size": 5132,
+        "fake_samples": 30123,
+        "real_samples": 21196
+    },
+    "runtime_hours": 0.4279349425766203
+}