Instructions to use jmeadows17/MathT5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jmeadows17/MathT5-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("jmeadows17/MathT5-base") model = AutoModelForSeq2SeqLM.from_pretrained("jmeadows17/MathT5-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: openrail
license: openrail pipeline_tag: text-generation
To use MathT5 easily:
- Download
MathT5.py. from MathT5 import load_model, inferencetokenizer, model = load_model("jmeadows17/MathT5-base")inference(prompt, tokenizer, model)
MathT5.pretty_print(text, prompt=True) makes prompts and outputs (prompt=False) easier to read.
Overview
MathT5-base is a version of T5-base (not FLAN-T5) that is fine-tuned for 25 epochs on 15K (LaTeX) synthetic mathematical derivations (containing 4 - 10 equations), that were generated using a symbolic solver (SymPy). Paper available here: https://arxiv.org/abs/2307.09998.
Example prompt:
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.
For the large version based on FLAN-T5 use ```"jmeadows17/MathT5-large"```.