metadata
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
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: https://huggingface.co/teknology/ad-classifier-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.