# 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)
```
{"language": ["en"], "license": "mit", "tags": ["nlp", "math learning", "education"]}
transformers pytorch roberta text-generation nlp math learning education en license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #roberta #text-generation #nlp #math learning #education #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 

# 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 (URL 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

# 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 (URL 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
TAGS
#transformers #pytorch #roberta #text-generation #nlp #math learning #education #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # 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 (URL 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
passage: TAGS
#transformers #pytorch #roberta #text-generation #nlp #math learning #education #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
# 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 (URL 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