metadata
language: en
datasets:
- Spotify Podcasts Dataset
tags:
- bert
- classification
- pytorch
pipeline:
- text-classification
General Information
This is a bert-base-cased, binary classification model, fine-tuned to classify a given sentence as containing advertising content or not. It leverages previous-sentence context to make more accurate predictions.
The model is used in the paper 'Leveraging multimodal content for podcast summarization' published at ACM SAC 2022.
Usage:
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}")
The manually annotated data, used for model fine-tuning are available here