MathBERT

MathBERT is a BERT model trained on the below mathematics text.

• pre-k to high school math curriculum from engageny.org
• G6-8 math curriculum from utahmiddleschoolmath.org
• G6-high school math from illustrativemathematics.org
• high school to college math text books from openculture.com
• G6-8 math curriculum from ck12.org
• College to graduate level MOOC math course syllabus from classcentral.com
• math paper abstracts from arxiv.org

MathBERT has its own vocabulary (mathVocab) that's built via BertTokenizer to best match the training corpus. We also trained MathBERT with the original BERT vocabulary (baseVocab) for comparison. Both models are uncased versions.

Downloading Trained Models

We release the tensorflow and the pytorch version of the trained models. The tensorflow version is compatible with code that works with the model from Google Research. The pytorch version is created using the Hugging Face library.

• Tensorflow download
  • note: to download mathbert-mathvocab version, change the model name to mathbert-mathvocab-uncased in the below code

  wget http://tracy-nlp-models.s3.amazonaws.com/mathbert-basevocab-uncased/bert_config.json
  wget http://tracy-nlp-models.s3.amazonaws.com/mathbert-basevocab-uncased/vocab.txt
  wget http://tracy-nlp-models.s3.amazonaws.com/mathbert-basevocab-uncased/bert_model.ckpt.index
  wget http://tracy-nlp-models.s3.amazonaws.com/mathbert-basevocab-uncased/bert_model.ckpt.meta
  wget http://tracy-nlp-models.s3.amazonaws.com/mathbert-basevocab-uncased/bert_model.ckpt.data-00000-of-00001
  

• Pytorch download
  • MathBERT models now can be installable directly within Huggingface's framework under the name space tbs17 at https://huggingface.co/tbs17/MathBERT or https://huggingface.co/tbs17/MathBERT-custom.

from transformers import *

tokenizer = AutoTokenizer.from_pretrained('tbs17/MathBERT')
model = AutoModel.from_pretrained('tbs17/MathBERT')

tokenizer = AutoTokenizer.from_pretrained('tbs17/MathBERT-custom')
model = AutoModel.from_pretrained('tbs17/MathBERT-custom')

Pretraining and fine-tuning

The pretraining code is located at /mathbert/ and fine-tuning notebook is at /scripts/MathBERT_finetune.ipynb. Unfortunately, we can't release the fine-tuning data set per the data owner's request. All the packages we use is in the requirements.txt file.

Legal

MathBERT is MIT-licensed, refer to the LICENSE file in the top level directory.