readme: ---
  language: 
  - en
  tags:
  - nlp
  - math learning
  - education
  license: mit
  ---
  # Math-RoBerta for NLP tasks in math learning environments
  
  This model is fine-tuned RoBERTa-large trained with 8 Nvidia RTX 1080Ti GPUs using 3,000,000 math discussion posts by students and facilitators on Algebra Nation (https://www.mathnation.com/). MathRoBERTa has 24 layers, and 355 million parameters and its published model weights take up to 1.5 gigabytes of disk space. It can potentially provide a good base performance on NLP related tasks (e.g., text classification, semantic search, Q&A) in similar math learning environments.
  
  ### Here is how to use it with texts in HuggingFace
  ```python
  from transformers import RobertaTokenizer, RobertaModel
  tokenizer = RobertaTokenizer.from_pretrained('uf-aice-lab/math-roberta')
  model = RobertaModel.from_pretrained('uf-aice-lab/math-roberta')
  text = "Replace me by any text you'd like."
  encoded_input = tokenizer(text, return_tensors='pt')
  output = model(**encoded_input)
  ```