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---
license: openrail
pipeline_tag: text-generation
---
**To use MathT5 easily:**
1. Download ```MathT5.py```.
2. ```from MathT5 import load_model, inference```
3. ```tokenizer, model = load_model("jmeadows17/MathT5-large")```
4. ```inference(prompt, tokenizer, model)```
```MathT5.pretty_print(text, prompt=True)``` makes prompts and outputs (```prompt=False```) easier to read.
**Overview**
MathT5-large is a version of FLAN-T5-large fine-tuned for 25 epochs on 15K (LaTeX) synthetic mathematical derivations (containing 4 - 10 equations), that were generated using a symbolic solver (SymPy).
It outperforms the few-shot performance of GPT-4 and ChatGPT on a derivation generation task in ROUGE, BLEU, BLEURT, and GLEU scores, and shows some generalisation capabilities.
It was trained on 155 physics symbols, but struggles with out-of-vocabulary symbols. Paper available here: https://arxiv.org/abs/2307.09998.
**Example prompt:**
```prompt = "Given \\cos{(q)} = \\theta{(q)},
then derive - \\sin{(q)} = \\frac{d}{d q} \\theta{(q)},
then obtain (- \\sin{(q)})^{q} (\\frac{d}{d q} \\cos{(q)})^{q} = (- \\sin{(q)})^{2 q}"```
Output derivations are equations separated by "and".
Additional prompts can be found in "training_prompts.json" alongside the model files.
Use ```"jmeadows17/MathT5-base"``` for the lightweight version. |