algebra_linear_1d_composed


language: en datasets:


This is a t5-small fine-tuned version on the math_dataset/algebra_linear_1d_composed for solving algebra linear 1d composed equations mission.

To load the model: (necessary packages: !pip install transformers sentencepiece)

from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("dbernsohn/algebra_linear_1d_composed")
model = AutoModelWithLMHead.from_pretrained("dbernsohn/algebra_linear_1d_composed")

You can then use this model to solve algebra 1d equations into numbers.

query = "Suppose -d = 5 - 16. Let b = -579 + 584. Solve -b*c + 36 = d for c."
input_text = f"{query} </s>"
features = tokenizer([input_text], return_tensors='pt')
model.to('cuda')
output = model.generate(input_ids=features['input_ids'].cuda(), 
                        attention_mask=features['attention_mask'].cuda())

tokenizer.decode(output[0])
# <pad> 5</s>

Another examples:









The whole training process and hyperparameters are in my GitHub repo

Created by Dor Bernsohn