Update README.md

README.md

@@ -1,3 +1,84 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- microsoft/deberta-v3-base
+---
+# Slop Classifier for Roleplay Characters
+
+> This model can detect characters that are created using AI.
+
+Part of [CharGen](https://huggingface.co/kubernetes-bad/chargen-v2) project - it is used to detect and filter out low-effort, LLM-made characters intended for role playing.
+
+*Slop* refers to over-used phrases that models like GPT3.5 like to use very much and that do not add any value to the text. "Shivers down her spine", "enigma wrapped in mystery", "half-lidded eyes", etc. Classifier is trained on set of synthetic characters generated with GPT3.5 and GPT4, and a substet of CharGen dataset.
+
+## Usage
+
+```py
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+from litserve import LitAPI, LitServer
+
+MODEL_NAME = "kubernetes-bad/character-slop-classifier"
+
+class CHARLitAPI(LitAPI):
+    def setup(self, device):
+        self.tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+        self.model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+        self.model.to(device)
+        self.model.eval()
+
+    def decode_request(self, request):
+        if "text" in request:
+            inputs = self.tokenizer(request["text"], return_tensors="pt", padding=True, truncation=True, max_length=512)
+        elif "texts" in request:
+            inputs = self.tokenizer(request["texts"], return_tensors="pt", padding=True, truncation=True, max_length=512)
+        else:
+            raise ValueError("Invalid request format. Expected 'text' or 'texts' field.")
+        return inputs
+
+    def predict(self, inputs):
+        with torch.no_grad():
+            inputs = {k: v.to(self.model.device) for k, v in inputs.items()}
+            outputs = self.model(**inputs)
+        return outputs.logits
+
+    def encode_response(self, logits):
+        probabilities = torch.nn.functional.softmax(logits, dim=-1)
+        if probabilities.shape[0] == 1:
+            response = {
+                "positive": probabilities[:, 1].item(),
+                "negative": probabilities[:, 0].item()
+            }
+        else:
+            response = [
+                {
+                    "positive": prob[1].item(),
+                    "negative": prob[0].item()
+                }
+                for prob in probabilities
+            ]
+        return response
+
+
+if __name__ == "__main__":
+    api = CHARLitAPI()
+    server = LitServer(api, accelerator='cuda')
+    server.run(port=9000)
+```
+
+```bash
+curl --location 'http://localhost:9000/predict' \
+--header 'Content-Type: application/json' \
+--data '{
+    "text": "Hermione, the seductive intellectual enchantress, is the secret sin of Hogwarts. Beneath her seemingly innocent scholarly facade lies a tantalizing world of forbidden desires. In the hallowed halls of the wizarding world, she conceals her lewd nature from her peers, maintaining a pristine reputation as the most brilliant witch of her age."
+}'
+```
+Example response:
+```json
+{
+    "positive": 0.9975564479827881,
+    "negative": 0.0024435613304376602
+}
+```