Mfaq

🚀 MFAQ

我们推出了一个多语言的常见问题解答（FAQ）检索模型，该模型基于 MFAQ 数据集 进行训练，它可以根据给定的问题对候选答案进行排序。

🚀 快速开始

MFAQ 是一个多语言的 FAQ 检索模型，它能依据给定问题对候选答案进行排序，为用户提供准确的问答匹配。

✨ 主要特性

• 多语言支持：支持捷克语（cs）、丹麦语（da）、德语（de）、英语（en）等多种语言。
• 适用于多种场景：可用于句子相似度计算、特征提取等任务。

📦 安装指南

pip install sentence-transformers transformers

💻 使用示例

基础用法

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)

高级用法

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'])

📚 详细文档

• 训练：你可以在 此处 找到该模型的训练脚本。
• 开发人员：此模型由 Maxime De Bruyn、Ehsan Lotfi、Jeska Buhmann 和 Walter Daelemans 开发。

📄 许可证

本模型采用 Apache-2.0 许可证。

📚 引用信息

@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}
}