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--- |
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language: |
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- en |
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tags: |
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- nlp |
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- math learning |
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- education |
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license: mit |
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--- |
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# Math-RoBerta for NLP tasks in math learning environments |
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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. |
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### Here is how to use it with texts in HuggingFace |
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```python |
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from transformers import RobertaTokenizer, RobertaModel |
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tokenizer = RobertaTokenizer.from_pretrained('uf-aice-lab/math-roberta') |
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model = RobertaModel.from_pretrained('uf-aice-lab/math-roberta') |
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text = "Replace me by any text you'd like." |
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encoded_input = tokenizer(text, return_tensors='pt') |
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output = model(**encoded_input) |
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``` |
