Upload folder using huggingface_hub

README.md

@@ -1,87 +1,72 @@
----
-license: mit
-datasets:
-- wangkevin02/LMSYS-USP
-language:
-- en
-metrics:
-- accuracy
-base_model:
-- allenai/longformer-base-4096
----
-# AI Detect Model
-
-## Model Description
-
-The **AI Detect Model** is a binary classification model designed to determine whether a given text is AI-generated (label=1) or written by a human (label=0). This model plays a crucial role in providing AI detection rewards, helping to prevent reward hacking during Reinforcement Learning with Cycle Consistency (RLCC). For more details, please refer to [our paper](https://tongyi.aliyun.com/qianwen/?sessionId=ea3bbcf36a2346a0a7819b06fcb36a1c#).
-
-This model is built upon the [Longformer](https://huggingface.co/allenai/longformer-base-4096) architecture and trained using our proprietary [LMSYS-USP](https://huggingface.co/datasets/wangkevin02/LMSYS-USP) dataset. Specifically, in a dialogue context, texts generated by the assistant are labeled as AI-generated (label=1), while user-generated texts are assigned the opposite label (label=0).
-
-> *Note*: Our model is subject to the following constraints:
->
-> 1. **Maximum Context Length**: Supports up to **4,096 tokens**. Exceeding this may degrade performance; keep inputs within this limit for best results.
-> 2. **Language Limitation**: Optimized for English. Non-English performance may vary due to limited training data.
-
-
-
-## Quick Start
-
-You can utilize our AI detection model as demonstrated below:
-
-```python
-from transformers import LongformerTokenizer, LongformerForSequenceClassification
-import torch
-import torch.nn.functional as F
-
-class AIDetector:
-    def __init__(self, model_name="allenai/longformer-base-4096", max_length=4096):
-        """
-        Initialize the AIDetector with a pretrained Longformer model and tokenizer.
-
-        Args:
-            model_name (str): The name or path of the pretrained Longformer model.
-            max_length (int): The maximum sequence length for tokenization.
-        """
-        self.tokenizer = LongformerTokenizer.from_pretrained(model_name)
-        self.model = LongformerForSequenceClassification.from_pretrained(model_name)
-        self.model.eval()
-        self.max_length = max_length
-        self.tokenizer.padding_side = "right"
-
-    @torch.no_grad()
-    def get_probability(self, texts):
-        inputs = self.tokenizer(texts, padding=True, truncation=True, max_length=self.max_length, return_tensors='pt')
-        outputs = self.model(**inputs)
-        probabilities = F.softmax(outputs.logits, dim=1)
-        return probabilities
-
-# Example usage
-if __name__ == "__main__":
-    """
-    Demonstrate the usage of AIDetector to classify whether given texts are AI-generated.
-    """
-    # Initialize the detector with a custom model path
-    classifier = AIDetector(model_name="/path/to/ai_detector")
-    
-    # Define sample texts for classification
-    target_text = [
-        "I am thinking about going away for vacation",
-        "How can I help you today?"
-    ]
-    
-    # Get classification probabilities
-    result = classifier.get_probability(target_text)
-    
-    # Print results
-    print("Classification Probabilities:", result)
-```
-
-
-
-## Citation
-
-If you find this model useful, please cite:
-
-```plaintext
-[Authors], "[Paper Title]," [Venue], [Year], [URL or DOI].
-```
+# AI Detect Model
+
+## Model Description
+
+The **AI Detect Model** is a binary classification model designed to determine whether a given text is AI-generated (label=1) or written by a human (label=0). This model plays a crucial role in providing AI detection rewards, helping to prevent reward hacking during Reinforcement Learning with Cycle Consistency (RLCC). For more details, please refer to [our paper](https://tongyi.aliyun.com/qianwen/?sessionId=ea3bbcf36a2346a0a7819b06fcb36a1c#).
+
+This model is built upon the [Longformer](https://huggingface.co/allenai/longformer-base-4096) architecture and trained using our proprietary [LMSYS-USP](https://huggingface.co/datasets/wangkevin02/LMSYS-USP) dataset. Specifically, in a dialogue context, texts generated by the assistant are labeled as AI-generated (label=1), while user-generated texts are assigned the opposite label (label=0).
+
+> *Note*: Our model is subject to the following constraints:
+>
+> 1. **Maximum Context Length**: Supports up to **4,096 tokens**. Exceeding this may degrade performance; keep inputs within this limit for best results.
+> 2. **Language Limitation**: Optimized for English. Non-English performance may vary due to limited training data.
+
+
+
+## Quick Start
+
+You can utilize our AI detection model as demonstrated below:
+
+```python
+from transformers import LongformerTokenizer, LongformerForSequenceClassification
+import torch
+import torch.nn.functional as F
+
+class AIDetector:
+    def __init__(self, model_name="allenai/longformer-base-4096", max_length=4096):
+        """
+        Initialize the AIDetector with a pretrained Longformer model and tokenizer.
+
+        Args:
+            model_name (str): The name or path of the pretrained Longformer model.
+            max_length (int): The maximum sequence length for tokenization.
+        """
+        self.tokenizer = LongformerTokenizer.from_pretrained(model_name)
+        self.model = LongformerForSequenceClassification.from_pretrained(model_name)
+        self.model.eval()
+        self.max_length = max_length
+        self.tokenizer.padding_side = "right"
+
+    @torch.no_grad()
+    def get_probability(self, texts):
+        inputs = self.tokenizer(texts, padding=True, truncation=True, max_length=self.max_length, return_tensors='pt')
+        outputs = self.model(**inputs)
+        probabilities = F.softmax(outputs.logits, dim=1)
+        return probabilities
+
+# Example usage
+if __name__ == "__main__":
+    classifier = AIDetector(model_name="/path/to/ai_detector")
+    target_text = [
+        "I am thinking about going away for vacation",
+        "How can I help you today?"
+        ]
+    result = classifier.get_probability(target_text)
+    print(result)
+    # >>> Expected Output:
+    # >>> tensor([[0.9954, 0.0046],
+    # >>>         [0.0265, 0.9735]])    
+```
+
+
+
+## Citation
+
+If you find this model useful, please cite:
+
+```plaintext
+[Authors], "[Paper Title]," [Venue], [Year], [URL or DOI].
+```
+
+
+