
## Model description 
This model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.

## How to use

```python
from transformers.pipelines import pipeline
model_name = "JAlexis/ajuste_01"
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
inputs = {
    'question': 'What are the risk factors for covid-19?',
    'context': 'To identify risk factors for hospital deaths from COVID-19, the OpenSAFELY platform examined electronic health records from 17.4 million UK adults. The authors used multivariable Cox proportional hazards model to identify the association of risk of death with older age, lower socio-economic status, being male, non-white ethnic background and certain clinical conditions (diabetes, obesity, cancer, respiratory diseases, heart, kidney, liver, neurological and autoimmune conditions). Notably, asthma was identified as a risk factor, despite prior suggestion of a potential protective role. Interestingly, higher risks due to ethnicity or lower socio-economic status could not be completely attributed to pre-existing health conditions.',
}
nlp(inputs)
```

{"widget": [{"text": "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. "}, {"text": "What are the risk factors for covid-19?", "context": "To identify risk factors for hospital deaths from COVID-19, the OpenSAFELY platform examined electronic health records from 17.4 million UK adults. The authors used multivariable Cox proportional hazards model to identify the association of risk of death with older age, lower socio-economic status, being male, non-white ethnic background and certain clinical conditions (diabetes, obesity, cancer, respiratory diseases, heart, kidney, liver, neurological and autoimmune conditions). Notably, asthma was identified as a risk factor, despite prior suggestion of a potential protective role. Interestingly, higher risks due to ethnicity or lower socio-economic status could not be completely attributed to pre-existing health conditions."}]}
transformers pytorch bert question-answering endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us 


## Model description 
This model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.

## How to use



## Model description 
This model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset. ## How to use
TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us 
 ## Model description 
This model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset. ## How to use
passage: TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us 
## Model description 
This model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.## How to use