Create README.md

README.md

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+**General Information**
+
+This is a BERT-based (base) classification model that is used to classify a given sentence as containing advertising content or not.
+The model is used in the paper 'Leveraging multimodal content for podcast summarization' published at ACM SAC 2022.
+
+**Usage:**
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+model = AutoModelForSequenceClassification.from_pretrained('morenolq/spotify-podcast-advertising-classification')
+tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
+
+desc_sentences = ["Sentence 1", "Sentence 2", "Sentence 3"]
+for i, s in enumerate(desc_sentences): 
+    if i==0:
+        context = "__START__"
+    else:
+        context = desc_sentences[i-1] 
+    out = tokenizer(context, text, padding = "max_length",
+                        max_length = 256,
+                        truncation=True,
+                        return_attention_mask=True,
+                        return_tensors = 'pt')
+    outputs = model(**out)
+    print (f"{s},{outputs}")
+```