| # algebra_linear_1d | |
| --- | |
| language: en | |
| datasets: | |
| - algebra_linear_1d | |
| --- | |
| This is a [t5-small](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned version on the [math_dataset/algebra_linear_1d](https://www.tensorflow.org/datasets/catalog/math_dataset#mathdatasetalgebra_linear_1d_default_config) for solving **algebra 1d equations** mission. | |
| To load the model: | |
| (necessary packages: !pip install transformers sentencepiece) | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelWithLMHead | |
| tokenizer = AutoTokenizer.from_pretrained("dbernsohn/algebra_linear_1d") | |
| model = AutoModelWithLMHead.from_pretrained("dbernsohn/algebra_linear_1d") | |
| ``` | |
| You can then use this model to solve algebra 1d equations into numbers. | |
| ```python | |
| 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 1112*r + 1418*r - 5220 = 587*r - 28536 for r. | |
| + Answer: -12 Pred: -12 | |
| ---- | |
| + Solve -119*k + 6*k - 117 - 352 = 322 for k. | |
| + Answer: -7 Pred: -7 | |
| ---- | |
| + Solve -547 = -62*t + 437 - 798 for t. | |
| + Answer: 3 Pred: 3 | |
| ---- | |
| + Solve 3*j - 3*j + 0*j - 4802 = 98*j for j. | |
| + Answer: -49 Pred: -49 | |
| ---- | |
| + Solve 3047*n - 6130*n - 1700 = -3049*n for n. | |
| + Answer: -50 Pred: -50 | |
| ---- | |
| + Solve 121*i + 1690 = 76*i - 128*i + 133 for i. | |
| + Answer: -9 Pred: -9 | |
| The whole training process and hyperparameters are in my [GitHub repo](https://github.com/DorBernsohn/CodeLM/tree/main/MathLM) | |
| > Created by [Dor Bernsohn](https://www.linkedin.com/in/dor-bernsohn-70b2b1146/) | |