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  ## Usage
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  ```
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification
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  tokenizer = AutoTokenizer.from_pretrained("albert-base-v2")
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  model = AutoModelForSequenceClassification.from_pretrained("AnReu/albert-for-math-ar-base-ft")
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- ```
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # ALBERT for Math AR
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+
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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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+
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  ## Usage
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  ```
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification
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  tokenizer = AutoTokenizer.from_pretrained("albert-base-v2")
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  model = AutoModelForSequenceClassification.from_pretrained("AnReu/albert-for-math-ar-base-ft")
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+ ```
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+
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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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+ ```
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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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+ year={2021},
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+ organization={CLEF}
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+ }
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+ ```