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--- |
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license: mit |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: bart-large-finetuned-filtered-spotify-podcast-summ |
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results: [] |
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--- |
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# bart-large-finetuned-filtered-spotify-podcast-summ |
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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. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 2.2967 |
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- Validation Loss: 2.8316 |
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- Epoch: 2 |
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## Intended uses & limitations |
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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. |
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## Training and evaluation data |
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In our solution, an extractive module is developed to select salient chunks from the transcript, which serve as the input to an abstractive summarizer. |
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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. |
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We split the filtered dataset into train/dev sets of 69,336/7,705 episodes. |
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The test set consists of 1,027 episodes. Only 1025 have been used because two of them did not contain an episode description. |
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## How to use |
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The model can be used for the summarization as follows: |
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```python |
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from transformers import pipeline |
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summarizer = pipeline("summarization", model="gmurro/bart-large-finetuned-filtered-spotify-podcast-summ", tokenizer="gmurro/bart-large-finetuned-filtered-spotify-podcast-summ") |
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summary = summarizer(podcast_transcript, min_length=39, max_length=250) |
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print(summary[0]['summary_text']) |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- 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} |
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- training_precision: float32 |
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### Training results |
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| Train Loss | Validation Loss | Epoch | |
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|:----------:|:---------------:|:-----:| |
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| 3.0440 | 2.8733 | 0 | |
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| 2.6085 | 2.8549 | 1 | |
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| 2.2967 | 2.8316 | 2 | |
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### Framework versions |
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- Transformers 4.19.4 |
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- TensorFlow 2.9.1 |
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- Datasets 2.3.1 |
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- Tokenizers 0.12.1 |
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## Authors |
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| Name | Surname | Email | Username | |
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| :-------: | :-------: | :------------------------------------: | :---------------------------------------------------: | |
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| Giuseppe | Boezio | `[email protected]` | [_giuseppeboezio_](https://github.com/giuseppeboezio) | |
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| Simone | Montali | `[email protected]` | [_montali_](https://github.com/montali) | |
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| Giuseppe | Murro | `[email protected]` | [_gmurro_](https://github.com/gmurro) | |
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