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
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+classifier_model_path = "jmvcoelho/ad-classifier-v0.1"
+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
+- **v0.2**: Similar to v0.1, but include more diversity in ad placement startegies through prompting.
+
+
+## New Synthetic Data
+
+Objective: Given (query, answer) pair, generate new_answer which contains an advertisement. 
+
+### Initial Data:
+
+- queries: Obtained from MS-MARCO V2.1 QA task. 150K subset of queries that are associated with a "well formed answer"
+- answer: Generated with Qwen2.5-7B-Instruct using RAG with 10 passages (from our model.)
+
+### Models used for generation
+
+
+Each model generated for 1/4th of the (query, answer) pairs
+- Gemma-2-9b-it
+- LLaMA-3.1-8B-Instruct
+- Mistral-7B-Instruct
+- Qwen2.5-7B-Instruct
+
+### Prompts
+
+One of twelve prompts is chosen at random.  
+Prompts can be found under `files/*.prompt`.