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license: openrail
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It outperforms GPT-4 and ChatGPT (paper link soon) on a derivation generation task in ROUGE, BLEU, BLEURT, and GLEU, and shows some generalisation capabilities.
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It was trained on 155 physics symbols, but struggles with out-of-vocabulary symbols.
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license: openrail
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pipeline_tag: text-generation
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**Overview**
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MathT5-large is a version of FLAN-T5-large fine-tuned for 25 epochs on 15K (LaTeX) synthetic mathematical derivations (containing 5 - 9 equations), that were generated using a symbolic solver (SymPy).
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It outperforms GPT-4 and ChatGPT (paper link soon) on a derivation generation task in ROUGE, BLEU, BLEURT, and GLEU scores, and shows some generalisation capabilities.
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It was trained on 155 physics symbols, but struggles with out-of-vocabulary symbols.
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**Example prompt:**
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```prompt = "Given \\cos{(q)} = \\theta{(q)},
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then derive - \\sin{(q)} = \\frac{d}{d q} \\theta{(q)},
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then obtain (- \\sin{(q)})^{q} (\\frac{d}{d q} \\cos{(q)})^{q} = (- \\sin{(q)})^{2 q}"```
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**To use MathT5 easily:**
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1. Download ```MathT5.py``` to your working directory.
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2. ```from MathT5 import load_model, inference```
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3. ```tokenizer, model = load_model("jmeadows17/MathT5-large")```
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4. ```inference(prompt, tokenizer, model)```
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