Update README.md

README.md

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-# LLM_Detector_Preview_model
-
-**Preview release of an LLM-generated text detector.**
-
-## Model Description
-This model is designed to classify text as Human, Mixed, or AI-generated. It is based on a sequence classification architecture and was trained on a mix of human and AI-generated texts. The model can be used for document, sentence, and token-level analysis.
-
-- **Architecture:** ModernBERT (or compatible Transformer)
-- **Labels:**
-  - 0: Human
-  - 1: Mixed
-  - 2: AI
-
-## Intended Use
-- **For research and curiosity only.**
-- Not for academic, legal, medical, or high-stakes use.
-- Results are easy to bypass and may be unreliable.
-
-## Limitations & Warnings
-- This model is **experimental** and not clinically accurate.
-- It can produce false positives and false negatives.
-- Simple paraphrasing or editing can fool the detector.
-- Do not use for academic integrity, hiring, or legal decisions.
-
-## How It Works
-The model analyzes text and predicts the likelihood of it being human-written, mixed, or AI-generated. It uses statistical patterns learned from training data, but these patterns are not foolproof and can be circumvented.
-
-## Example Usage
-```python
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch
-
-tokenizer = AutoTokenizer.from_pretrained('Donnyed/LLM_Detector_Preview_model')
-model = AutoModelForSequenceClassification.from_pretrained('Donnyed/LLM_Detector_Preview_model')
-
-text = "Paste your text here."
-inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
-with torch.no_grad():
-    outputs = model(**inputs)
-    probs = torch.softmax(outputs.logits, dim=1)
-    pred = torch.argmax(probs, dim=1).item()
-    print('Prediction:', pred)
-    print('Probabilities:', probs)
-```
-
-## Files Included
-- `model.safetensors` — Model weights
-- `config.json` — Model configuration
-- `tokenizer.json`, `tokenizer_config.json`, `special_tokens_map.json` — Tokenizer files
-
-## License
-Specify your license here (e.g., Apache 2.0, MIT, etc.)
-
----
-**Maintainer:** [your-username](https://huggingface.co/your-username) 
+# LLM_Detector_Preview_model
+
+**Preview release of an LLM-generated text detector.**
+
+## Model Description
+This model is designed to classify text as Human, Mixed, or AI-generated. It is based on a sequence classification architecture and was trained on a mix of human and AI-generated texts. The model can be used for document, sentence, and token-level analysis.
+
+- **Architecture:** ModernBERT (or compatible Transformer)
+- **Labels:**
+  - 0: Human
+  - 1: Mixed
+  - 2: AI
+
+## Intended Use
+- **For research and curiosity only.**
+- Not for academic, legal, medical, or high-stakes use.
+- Results are easy to bypass and may be unreliable.
+
+## Limitations & Warnings
+- This model is **experimental** and not clinically accurate.
+- It can produce false positives and false negatives.
+- Simple paraphrasing or editing can fool the detector.
+- Do not use for academic integrity, hiring, or legal decisions.
+
+## How It Works
+The model analyzes text and predicts the likelihood of it being human-written, mixed, or AI-generated. It uses statistical patterns learned from training data, but these patterns are not foolproof and can be circumvented.
+
+## Example Usage
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained('Donnyed/LLM_Detector_Preview_model')
+model = AutoModelForSequenceClassification.from_pretrained('Donnyed/LLM_Detector_Preview_model')
+
+text = "Paste your text here."
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+with torch.no_grad():
+    outputs = model(**inputs)
+    probs = torch.softmax(outputs.logits, dim=1)
+    pred = torch.argmax(probs, dim=1).item()
+    print('Prediction:', pred)
+    print('Probabilities:', probs)
+```
+
+## Files Included
+- `model.safetensors` — Model weights
+- `config.json` — Model configuration
+- `tokenizer.json`, `tokenizer_config.json`, `special_tokens_map.json` — Tokenizer files