Create README.md

README.md

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+---
+language:
+- en
+tags:
+- roberta
+- marketing mix
+- multi-label
+- classification
+- microblog
+- tweets
+---
+# Model Card for: mmx_classifier_microblog_ENv02
+
+Multi-label classifier that identifies which marketing mix variable(s) a microblog post pertains to.
+
+## Model Details
+
+You can use this classifier to determine which of the 4P's of marketing, also known as marketing mix variables, a microblog post (e.g., Tweet) pertains to:
+
+1. Product
+2. Place
+3. Price
+4. Promotion
+
+### Model Description
+
+This classifier is a fine-tuned checkpoint of [cardiffnlp/twitter-roberta-large-2022-154m] (https://huggingface.co/cardiffnlp/twitter-roberta-large-2022-154m). 
+It was trained on 15K Tweets that mentioned at least one of 699 brands. The Tweets were cleaned and labeled using OpenAI's GPT4. 
+
+Because this is a multi-label classification problem, we use binary cross-entropy (BCE) with logits loss for the fine-tuning. We basically combine a sigmoid layer with BCELoss in a single class.
+To obtain the probabilities for each label (i.e., marketing mix variable), you need to "push" the predictions through a sigmoid function. This is already done in the accompanying python notebook.
+
+IMPORTANT: At the time of writing this description, Huggingface's pipeline did not support multi-label classifiers.
+
+### Citation
+For attribution, please cite the following reference if you use this model:
+```
+Ringel, Daniel, Creating Synthetic Experts with Generative Artificial Intelligence (July 15, 2023). Available at SSRN: https://ssrn.com/abstract=4542949
+```
+
+### Additional Ressources
+[www.synthetic-experts.ai](http://www.synthetic-experts.ai)
+
+