
[Extractive Question Answering application.](https://raphaelsty.github.io/blog/template/)

```python
import re

from transformers import pipeline

qa = pipeline(
    "question-answering", model="raphaelsty/carbonblog", tokenizer="raphaelsty/carbonblog"
)

def clean(document):
    """Pre-process the document."""
    document = re.sub("[^a-zA-Z0-9 \n\.]", " ", document)
    document = re.sub("\s\s+", " ", document)
    # [NONE] allows the model to handle missing components.
    document = f"[NONE] {document.lower()}"
    return document
```

```python
document = clean("body: 83% nylon 17% spandex; lace: 86% nylon 14% spandex")
```

```python
qa({"question": "What is the 1 component ?", "context": document})
```

```json
{"score": 0.9999976754188538, "start": 32, "end": 36, "answer": "lace"}
```



{"language": ["en"], "license": "afl-3.0", "tags": ["feature-extraction"], "pipeline_tag": "question-answering"}
transformers pytorch roberta question-answering feature-extraction en license:afl-3.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #question-answering #feature-extraction #en #license-afl-3.0 #endpoints_compatible #region-us 


Extractive Question Answering application.











TAGS
#transformers #pytorch #roberta #question-answering #feature-extraction #en #license-afl-3.0 #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #roberta #question-answering #feature-extraction #en #license-afl-3.0 #endpoints_compatible #region-us 
