|  | --- | 
					
						
						|  | 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 | 
					
						
						|  | ``` |