readme: ---
  license: mit
  tags:
  - generated_from_keras_callback
  model-index:
  - name: bart-large-finetuned-filtered-spotify-podcast-summ
    results: []
  ---
  
  # bart-large-finetuned-filtered-spotify-podcast-summ
  
  This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on on the [Spotify Podcast Dataset](https://arxiv.org/abs/2004.04270). Take a look to the [github repository](https://github.com/TheOnesThatWereAbroad/PodcastSummarization) of this project.
  
  It achieves the following results during training:
  - Train Loss: 2.2967
  - Validation Loss: 2.8316
  - Epoch: 2
  
  ## Intended uses & limitations
  
  This model is intended to be used for automatic podcast summarisation. Given the podcast transcript in input, the objective is to provide a short text summary that a user might read when deciding whether to listen to a podcast. The summary should accurately convey the content of the podcast, be human-readable, and be short enough to be quickly read on a smartphone screen. 
  
  ## Training and evaluation data
  In our solution, an extractive module is developed to select salient chunks from the transcript, which serve as the input to an abstractive summarizer.
  An extensive pre-processing on the creator-provided descriptions is performed selecting a subset of the corpus that is suitable for the training supervised model.
  
  We split the filtered dataset into train/dev sets of 69,336/7,705 episodes. 
  The test set consists of 1,027 episodes. Only 1025 have been used because two of them did not contain an episode description.
  
  
  ## How to use
  
  The model can be used for the summarization as follows:
  
  ```python
  from transformers import pipeline
  summarizer = pipeline("summarization", model="gmurro/bart-large-finetuned-filtered-spotify-podcast-summ", tokenizer="gmurro/bart-large-finetuned-filtered-spotify-podcast-summ")
  summary = summarizer(podcast_transcript, min_length=39, max_length=250)
  print(summary[0]['summary_text'])
  ```
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - ```python
  optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
  ```
  - ```python
  training_precision: float32
  ```
  
  ### Training results
  
  | Train Loss | Validation Loss | Epoch |
  |:----------:|:---------------:|:-----:|
  | 3.0440     | 2.8733          | 0     |
  | 2.6085     | 2.8549          | 1     |
  | 2.2967     | 2.8316          | 2     |
  
  
  ### Framework versions
  
  - Transformers 4.19.4
  - TensorFlow 2.9.1
  - Datasets 2.3.1
  - Tokenizers 0.12.1
  
  
  ## Authors
  
  |   Name    |  Surname  |                 Email                  |                       Username                        |
  | :-------: | :-------: | :------------------------------------: | :---------------------------------------------------: |
  | Giuseppe  |   Boezio  | `giuseppe.boezio@studio.unibo.it`      | [_giuseppeboezio_](https://github.com/giuseppeboezio) |
  | Simone    |  Montali  |    `simone.montali@studio.unibo.it`    |         [_montali_](https://github.com/montali)         |
  | Giuseppe  |    Murro  |    `giuseppe.murro@studio.unibo.it`    |         [_gmurro_](https://github.com/gmurro)         |
  
  
  