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--- |
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language: "en" |
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datasets: |
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- Spotify Podcasts Dataset |
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tags: |
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- bert |
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- classification |
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- pytorch |
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pipeline: |
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- text-classification |
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--- |
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**General Information** |
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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. |
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The model is used in the paper 'Leveraging multimodal content for podcast summarization' published at ACM SAC 2022. |
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**Usage:** |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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model = AutoModelForSequenceClassification.from_pretrained('morenolq/spotify-podcast-advertising-classification') |
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tokenizer = AutoTokenizer.from_pretrained('bert-base-cased') |
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desc_sentences = ["Sentence 1", "Sentence 2", "Sentence 3"] |
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for i, s in enumerate(desc_sentences): |
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if i==0: |
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context = "__START__" |
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else: |
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context = desc_sentences[i-1] |
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out = tokenizer(context, text, padding = "max_length", |
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max_length = 256, |
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truncation=True, |
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return_attention_mask=True, |
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return_tensors = 'pt') |
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outputs = model(**out) |
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print (f"{s},{outputs}") |
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``` |
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The manually annotated data, used for model fine-tuning are available [here](https://github.com/MorenoLaQuatra/MATeR/blob/main/description_sentences_classification.tsv) |
