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--- |
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library_name: transformers |
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license: mit |
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language: |
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- en |
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pipeline_tag: summarization |
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--- |
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# BART Base Text Summarization Modeli |
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<!-- Provide a quick summary of what the model is/does. --> |
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This model is based on the Facebook BART (Bidirectional and Auto-Regressive Transformers) architecture. BART is particularly effective when fine-tuned for text generation tasks like summarization but also works well for comprehension tasks. |
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BART is a transformer encoder-decoder (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, |
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and (2) learning a model to reconstruct the original text. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. |
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- **Architecture:** [BART Base] |
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- **Pre-trained model:** [facebook/bart-base] |
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- **Fine-tuned for:** [Summarization] |
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- **License:** [MIT] |
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- **Finetuned from model:** [facebook/bart-base] |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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- **Installation:** |
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pip install transformers |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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Here is a simple snippet oon how to use the model directly. |
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# Load model directly |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("ChijoTheDatascientist/summarization-model") |
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model = AutoModelForSeq2SeqLM.from_pretrained("ChijoTheDatascientist/summarization-model") |
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