Update README.md

README.md

@@ -44,8 +44,47 @@ To obtain the probabilities for each label (i.e., marketing mix variable), you n
 
 IMPORTANT: At the time of writing this description, Huggingface's pipeline did not support multi-label classifiers.
 
+### Quickstart
+
+```python
+# Imports
+import pandas as pd, numpy as np, warnings, torch, re
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from bs4 import BeautifulSoup
+warnings.filterwarnings("ignore", category=UserWarning, module='bs4')
+
+# Helper Functions
+def clean_and_parse_tweet(tweet):
+    tweet = re.sub(r"https?://\S+|www\.\S+", " URL ", tweet)
+    parsed = BeautifulSoup(tweet, "html.parser").get_text() if "filename" not in str(BeautifulSoup(tweet, "html.parser")) else None
+    return re.sub(r" +", " ", re.sub(r'^[.:]+', '', re.sub(r"\\n+|\n+", " ", parsed or tweet)).strip()) if parsed else None
+
+def predict_tweet(tweet, model, tokenizer, device, threshold=0.5):
+    inputs = tokenizer(tweet, return_tensors="pt", padding=True, truncation=True, max_length=128).to(device)
+    probs = torch.sigmoid(model(**inputs).logits).detach().cpu().numpy()[0]
+    return probs, [id2label[i] for i, p in enumerate(probs) if id2label[i] in {'Product', 'Place', 'Price', 'Promotion'} and p >= threshold]
+
+# Setup
+device = "mps" if torch.backends.mps.is_built() and torch.backends.mps.is_available() else "cuda" if torch.cuda.is_available() else "cpu"
+synxp = "dmr76/mmx_classifier_microblog_ENv02"
+model = AutoModelForSequenceClassification.from_pretrained(synxp).to(device)
+tokenizer = AutoTokenizer.from_pretrained(synxp)
+id2label = model.config.id2label
+
+# ---->>> Define your Tweet  <<<----
+tweet = "Best cushioning ever!!! 🤗🤗🤗  my zoom vomeros are the bomb🏃🏽‍♀️💨!!!  \n @nike #run #training https://randomurl.ai"
+
+# Clean and Predict
+cleaned_tweet = clean_and_parse_tweet(tweet)
+probs, labels = predict_tweet(cleaned_tweet, model, tokenizer, device)
+
+# Print Labels and Probabilities
+print("Please don't forget to cite the paper: https://ssrn.com/abstract=4542949")
+print(labels, probs)
+```
+
 ### Citation
-For attribution, please cite the following reference if you use this model:
+Please cite the following reference if you use synthetic experts in your work:
 ```
 Ringel, Daniel, Creating Synthetic Experts with Generative Artificial Intelligence (July 15, 2023). Available at SSRN: https://ssrn.com/abstract=4542949
 ```