---
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
license: mit
tags:
- text-classification
- sequence-classification
- xlm-roberta-base
- faq
- questions
datasets:
- clips/mfaq
- daily_dialog
- tau/commonsense_qa
- conv_ai_2
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
pipeline_tag: text-classification
---
## Frequently Asked Questions classifier
This model is trained to determine whether a question/statement is a FAQ, in the domain of products, businesses, website faqs, etc. 
For e.g `"What is the warranty of your product?"` In contrast, daily questions such as `"how are you?"`, `"what is your name?"`, or simple statements such as `"this is a tree"`.

## Usage
```python
from transformers import pipeline

classifier = pipeline("text-classification", "timpal0l/xlm-roberta-base-faq-extractor")
label_map = {"LABEL_0" : False, "LABEL_1" : True}

documents = ["What is the warranty for iPhone15?", 
             "How old are you?", 
             "Nice to meet you", 
             "What is your opening hours?", 
             "What is your name?", 
             "The weather is nice"]

predictions = classifier(documents)

for p, d in zip(predictions, documents):
    print(d, "--->", label_map[p["label"]])
```

```html
What is the warranty for iPhone15? ---> True
How old are you? ---> False
Nice to meet you ---> False
What is your opening hours? ---> True
What is your name? ---> False
The weather is nice ---> False
```