Instructions to use mabrouk/amazon-review-summarizer-bart with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mabrouk/amazon-review-summarizer-bart with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("mabrouk/amazon-review-summarizer-bart") model = AutoModelForSeq2SeqLM.from_pretrained("mabrouk/amazon-review-summarizer-bart", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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by Alvinnebula - opened
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According to huggingface, BART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
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## Intended uses & limitations
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This model is intended to be used for summarizing user reviews on websites.
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According to huggingface, BART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
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## Datasets
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Link: [Amazon Reviews Corpus](https://huggingface.co/datasets/amazon_reviews_multi)
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## Intended uses & limitations
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This model is intended to be used for summarizing user reviews on websites.
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