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  This model is further pre-trained on the Mathematics StackExchange questions and answers. It is based on Albert base v2 and uses the same tokenizer. In addition to pre-training the model was finetuned on Math Question Answer Retrieval. The sequence classification head is trained to output a relevance score if you input the question as the first segment and the answer as the second segment. You can use the relevance score to rank different answers for retrieval.
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  ## Usage
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- ```
 
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification
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  import torch
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  ## Reference
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  If you use this model, please consider referencing our paper:
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- ```
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  @inproceedings{reusch2021tu_dbs,
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  title={TU\_DBS in the ARQMath Lab 2021, CLEF},
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  author={Reusch, Anja and Thiele, Maik and Lehner, Wolfgang},
 
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  This model is further pre-trained on the Mathematics StackExchange questions and answers. It is based on Albert base v2 and uses the same tokenizer. In addition to pre-training the model was finetuned on Math Question Answer Retrieval. The sequence classification head is trained to output a relevance score if you input the question as the first segment and the answer as the second segment. You can use the relevance score to rank different answers for retrieval.
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  ## Usage
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+ ```python
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+ # based on https://huggingface.co/docs/transformers/main/en/task_summary#sequence-classification
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification
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  import torch
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  ## Reference
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  If you use this model, please consider referencing our paper:
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+ ```bibtex
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  @inproceedings{reusch2021tu_dbs,
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  title={TU\_DBS in the ARQMath Lab 2021, CLEF},
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  author={Reusch, Anja and Thiele, Maik and Lehner, Wolfgang},