Create README.md

README.md

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+---
+language:
+- en
+base_model:
+- microsoft/deberta-v3-base
+pipeline_tag: text-classification
+---
+Binary classification model for ad-detection on QA Systems.
+
+## Sample usage
+
+```
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+classifier_model_path = "jmvcoelho/ad-classifier-v0.0"
+tokenizer = AutoTokenizer.from_pretrained(classifier_model_path)
+model = AutoModelForSequenceClassification.from_pretrained(classifier_model_path)
+model.eval()
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+def classify(passages):
+    inputs = tokenizer(
+        passages, padding=True, truncation=True, max_length=512, return_tensors="pt"
+    )
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        predictions = torch.argmax(logits, dim=-1)
+    return predictions.cpu().tolist()
+
+preds = classify(["sample_text_1", "sample_text_2"])
+```
+
+
+## Version
+
+- **v0.0:** Trained with the official data from Webis Generated Native Ads 2024
+- v0.1: Trained with v0.0 data + new synthetic data
+
+
+## Webis Generated Native Ads 2024
+
+**Paper:**  [Detecting Generated Native Ads in Conversational Search](https://dl.acm.org/doi/10.1145/3589335.3651489)
+
+**Data summary:**
+- YouChat and Microsoft Copilot were used to generate answers for competitve keywork queries;
+- GPT-4 turbo was used to insert one advertisment into the answer;
+- This creates triples (query, answer_with_ad, answer_without_ad)
+- The classifier in this repo was trained to assign 0 to answer_without_ad, and 1 to answer_with_ad.