readme: ---
  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}
  }
  ```