Create README.md

README.md

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+# 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 translate SQL queries into plain english.
+
+```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>
+
+Solve 1112*r + 1418*r - 5220 = 587*r - 28536 for r.
+Answer:  -12 Pred:  -12
+----
+Solve -547 = -62*t + 437 - 798 for t.
+Answer:  3 Pred:  -1
+----
+Solve -119*k + 6*k - 117 - 352 = 322 for k.
+Answer:  -7 Pred:  -7
+----
+Solve 3*j - 3*j + 0*j - 4802 = 98*j for j.
+Answer:  -49 Pred:  -47
+----
+Solve 3047*n - 6130*n - 1700 = -3049*n for n.
+Answer:  -50 Pred:  -25
+----
+Solve 121*i + 1690 = 76*i - 128*i + 133 for i.
+Answer:  -9 Pred:  -7
+```
+
+
+> Created by [Dor Bernsohn](https://www.linkedin.com/in/dor-bernsohn-70b2b1146/)