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--- |
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language: en |
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tags: |
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- pytorch |
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- question-answering |
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datasets: |
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- squad2 |
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- cord19 |
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metrics: |
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- f1 |
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widget: |
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- text: "How can I protect myself against covid-19?" |
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context: "Preventative measures consist of recommendations to wear a mask in public, maintain social distancing of at least six feet, wash hands regularly, and use hand sanitizer. To facilitate this aim, we adapt the conceptual model and measures of Liao et al. [6] to the current context of the COVID-19 pandemic and the culture of the USA. Applying this model in a different time and context provides an opportunity to make comparisons of reactions to information sources across a decade of evolving attitudes toward media and government, between two cultures (Hong Kong vs. the USA), and between two considerably different global pandemics (H1N1 vs. COVID-19)." |
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- text: "How can I protect myself against covid-19?" |
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context: " " |
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--- |
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## Model description |
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This model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset. |
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## How to use |
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```python |
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from transformers.pipelines import pipeline |
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model_name = "JAlexis/PruebaBert" |
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nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) |
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inputs = { |
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'question': 'How can I protect myself against covid-19?', |
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'context': 'Preventative measures consist of recommendations to wear a mask in public, maintain social distancing of at least six feet, wash hands regularly, and use hand sanitizer. To facilitate this aim, we adapt the conceptual model and measures of Liao et al. [6] to the current context of the COVID-19 pandemic and the culture of the USA. Applying this model in a different time and context provides an opportunity to make comparisons of reactions to information sources across a decade of evolving attitudes toward media and government, between two cultures (Hong Kong vs. the USA), and between two considerably different global pandemics (H1N1 vs. COVID-19). ', |
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'question': 'How can I protect myself against covid-19?', |
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'context': ' ', |
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} |
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nlp(inputs) |
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``` |
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## Overview |
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``` |
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Language model: deepset/bert-base-cased-squad2 |
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Language: English |
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Downstream-task: Q&A |
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Datasets: CORD-19 from 31rd January 2022 |
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Code: Haystack and FARM |
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Infrastructure: Tesla T4 |
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``` |
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## Hyperparameters |
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``` |
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batch_size = 8 |
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n_epochs = 7 |
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max_seq_len = max_length |
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learning_rate = AdamW: 2e-5 |
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``` |
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