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README.md
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# algebra_linear_1d
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---
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language: en
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datasets:
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- algebra_linear_1d
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---
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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.
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To load the model:
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(necessary packages: !pip install transformers sentencepiece)
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```python
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from transformers import AutoTokenizer, AutoModelWithLMHead
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tokenizer = AutoTokenizer.from_pretrained("dbernsohn/algebra_linear_1d")
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model = AutoModelWithLMHead.from_pretrained("dbernsohn/algebra_linear_1d")
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```
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You can then use this model to translate SQL queries into plain english.
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```python
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query = "Solve 0 = 1026*x - 2474 + 46592 for x"
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input_text = f"{query} </s>"
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features = tokenizer([input_text], return_tensors='pt')
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model.to('cuda')
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output = model.generate(input_ids=features['input_ids'].cuda(),
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attention_mask=features['attention_mask'].cuda())
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tokenizer.decode(output[0])
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# <pad> -41</s>
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Solve 1112*r + 1418*r - 5220 = 587*r - 28536 for r.
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Answer: -12 Pred: -12
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----
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Solve -547 = -62*t + 437 - 798 for t.
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Answer: 3 Pred: -1
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----
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Solve -119*k + 6*k - 117 - 352 = 322 for k.
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Answer: -7 Pred: -7
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----
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Solve 3*j - 3*j + 0*j - 4802 = 98*j for j.
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Answer: -49 Pred: -47
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----
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Solve 3047*n - 6130*n - 1700 = -3049*n for n.
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Answer: -50 Pred: -25
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----
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Solve 121*i + 1690 = 76*i - 128*i + 133 for i.
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Answer: -9 Pred: -7
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```
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> Created by [Dor Bernsohn](https://www.linkedin.com/in/dor-bernsohn-70b2b1146/)
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