Bertv1 Fine

🚀 JAlexis/PruebaBert

This is a question-answering model fine-tuned on the Cord19 Dataset, which can effectively answer relevant questions.

🚀 Quick Start

This model is a fine - tuned version of deepset/bert-base-cased-squad2 on the Cord19 Dataset. You can use it to answer questions as follows:

from transformers.pipelines import pipeline
model_name = "JAlexis/PruebaBert"
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
inputs = {
    'question': 'How can I protect myself against covid-19?',
    '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). ',
    'question': 'How can I protect myself against covid-19?',
    'context': ' ',
}
nlp(inputs)

✨ Features

• Language: English
• Downstream - task: Question - Answering
• Datasets: Utilizes the CORD - 19 dataset from 31rd January 2022.
• Code: Implemented with Haystack and FARM.
• Infrastructure: Runs on Tesla T4.

🔧 Technical Details

Hyperparameters

batch_size = 8
n_epochs = 7
max_seq_len = max_length
learning_rate = AdamW: 2e-5