mfaq / README.md

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	---
	pipeline_tag: sentence-similarity
	license: apache-2.0
	language:
	- cs
	- da
	- de
	- en
	- es
	- fi
	- fr
	- he
	- hr
	- hu
	- id
	- it
	- nl
	- 'no'
	- pl
	- pt
	- ro
	- ru
	- sv
	- tr
	- vi
	tags:
	- sentence-transformers
	- feature-extraction
	- sentence-similarity
	- transformers
	datasets:
	- clips/mfaq
	widget:
	source_sentence: "<Q>How many models can I host on HuggingFace?"
	sentences:
	- "<A>All plans come with unlimited private models and datasets."
	- "<A>AutoNLP is an automatic way to train and deploy state-of-the-art NLP models, seamlessly integrated with the Hugging Face ecosystem."
	- "<A>Based on how much training data and model variants are created, we send you a compute cost and payment link - as low as $10 per job."

	---

	# MFAQ

	We present a multilingual FAQ retrieval model trained on the [MFAQ dataset](https://huggingface.co/datasets/clips/mfaq), it ranks candidate answers according to a given question.

	## Installation

	```
	pip install sentence-transformers transformers
	```

	## Usage
	You can use MFAQ with sentence-transformers or directly with a HuggingFace model.
	In both cases, questions need to be prepended with `<Q>`, and answers with `<A>`.

	#### Sentence Transformers
	```python
	from sentence_transformers import SentenceTransformer

	question = "<Q>How many models can I host on HuggingFace?"
	answer_1 = "<A>All plans come with unlimited private models and datasets."
	answer_2 = "<A>AutoNLP is an automatic way to train and deploy state-of-the-art NLP models, seamlessly integrated with the Hugging Face ecosystem."
	answer_3 = "<A>Based on how much training data and model variants are created, we send you a compute cost and payment link - as low as $10 per job."

	model = SentenceTransformer('clips/mfaq')
	embeddings = model.encode([question, answer_1, answer_3, answer_3])
	print(embeddings)
	```

	#### HuggingFace Transformers

	```python
	from transformers import AutoTokenizer, AutoModel
	import torch

	def mean_pooling(model_output, attention_mask):
	token_embeddings = model_output[0] #First element of model_output contains all token embeddings
	input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
	return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

	question = "<Q>How many models can I host on HuggingFace?"
	answer_1 = "<A>All plans come with unlimited private models and datasets."
	answer_2 = "<A>AutoNLP is an automatic way to train and deploy state-of-the-art NLP models, seamlessly integrated with the Hugging Face ecosystem."
	answer_3 = "<A>Based on how much training data and model variants are created, we send you a compute cost and payment link - as low as $10 per job."

	tokenizer = AutoTokenizer.from_pretrained('clips/mfaq')
	model = AutoModel.from_pretrained('clips/mfaq')

	# Tokenize sentences
	encoded_input = tokenizer([question, answer_1, answer_3, answer_3], padding=True, truncation=True, return_tensors='pt')

	# Compute token embeddings
	with torch.no_grad():
	model_output = model(**encoded_input)

	# Perform pooling. In this case, max pooling.
	sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
	```

	## Training
	You can find the training script for the model [here](https://github.com/clips/mfaq).

	## People
	This model was developed by [Maxime De Bruyn](https://www.linkedin.com/in/maximedebruyn/), Ehsan Lotfi, Jeska Buhmann and Walter Daelemans.

	## Citation information
	```
	@misc{debruyn2021mfaq,
	title={MFAQ: a Multilingual FAQ Dataset},
	author={Maxime De Bruyn and Ehsan Lotfi and Jeska Buhmann and Walter Daelemans},
	year={2021},
	eprint={2109.12870},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	```