entity2260/pythia-70m

algebra_linear_1d

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language: en datasets: - algebra_linear_1d

This is a t5-small fine-tuned version on the math_dataset/algebra_linear_1d for solving algebra 1d equations mission.

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("entity2260/pythia-70m")
model = AutoModelForCausalLM.from_pretrained("entity2260/pythia-70m")

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

query = "Solve 0 = 1026*x - 2474 + 46592 for x"
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> -41</s>

Another examples:

• Solve 1112r + 1418r - 5220 = 587*r - 28536 for r.
• Answer: -12 Pred: -12

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• Solve -119k + 6k - 117 - 352 = 322 for k.
• Answer: -7 Pred: -7

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• Solve -547 = -62*t + 437 - 798 for t.
• Answer: 3 Pred: 3

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• Solve 3j - 3j + 0j - 4802 = 98j for j.
• Answer: -49 Pred: -49

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• Solve 3047n - 6130n - 1700 = -3049*n for n.
• Answer: -50 Pred: -50

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• Solve 121i + 1690 = 76i - 128*i + 133 for i.
• Answer: -9 Pred: -9