readme: ---
  language: "en"
  datasets:
  - spotify-podcast-dataset
  tags:
  - bert
  - classification
  - pytorch
  pipeline:
  - text-classification
  widget:
  - text: "__START__ [SEP] This is the first podcast on natural language processing applied to spoken language."
  - text: "This is the first podcast on natural language processing applied to spoken language. [SEP] You can find us on https://twitter.com/PodcastExampleClassifier."
  - text: "You can find us on https://twitter.com/PodcastExampleClassifier. [SEP] You can also subscribe to our newsletter https://newsletter.com/PodcastExampleClassifier."
  ---
  
  **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:**
  
  ```python
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  model = AutoModelForSequenceClassification.from_pretrained('morenolq/spotify-podcast-advertising-classification')
  tokenizer = AutoTokenizer.from_pretrained('morenolq/spotify-podcast-advertising-classification')
  
  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, s, 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](https://github.com/MorenoLaQuatra/MATeR/blob/main/description_sentences_classification.tsv)
  
  Hereafter is the classification report of the model evaluation on the test split:
  
  ```
                precision    recall  f1-score   support
  
             0       0.95      0.93      0.94       256
             1       0.88      0.91      0.89       140
  
      accuracy                           0.92       396
     macro avg       0.91      0.92      0.92       396
  weighted avg       0.92      0.92      0.92       396
  ```
  
  If you find it useful, please cite the following paper:
  
  ```bibtex
  @inproceedings{10.1145/3477314.3507106,
      author = {Vaiani, Lorenzo and La Quatra, Moreno and Cagliero, Luca and Garza, Paolo},
      title = {Leveraging Multimodal Content for Podcast Summarization},
      year = {2022},
      isbn = {9781450387132},
      publisher = {Association for Computing Machinery},
      address = {New York, NY, USA},
      url = {https://doi.org/10.1145/3477314.3507106},
      doi = {10.1145/3477314.3507106},
      booktitle = {Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing},
      pages = {863–870},
      numpages = {8},
      keywords = {multimodal learning, multimodal features fusion, extractive summarization, deep learning, podcast summarization},
      location = {Virtual Event},
      series = {SAC '22}
  }
  ```