Update README.md

README.md

@@ -1,3 +1,46 @@
----
-license: mit
----
+---
+language:
+- en
+base_model:
+- microsoft/deberta-v3-base
+pipeline_tag: text-classification
+license: mit
+---
+Binary classification model for ad-detection on QA Systems.
+
+## Sample usage
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+classifier_model_path = "teknology/ad-classifier-v0.4"
+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
+
+Previous versions can be found at:
+- v0.0: https://huggingface.co/jmvcoelho/ad-classifier-v0.0  
+  Trained with the official data from Webis Generated Native Ads 2024
+- v0.1: https://huggingface.co/jmvcoelho/ad-classifier-v0.1  
+  Trained with v0.0 data + new synthetic data
+- v0.2: https://huggingface.co/jmvcoelho/ad-classifier-v0.2  
+  Similar to v0.1, but include more diversity in ad placement startegies through prompting.
+- v0.3: Continued from v0.2, added a new synthetic dataset generated based on Wikipedia articles.
+- **v0.4**: Same training data composition as v0.3, but curriculum learning with the mixed data.