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Model: clips/mfaq
Source: Original Platform

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}
+}
+```