metadata
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 15268888.05
num_examples: 487500
- name: test
num_bytes: 391509.95
num_examples: 12500
download_size: 12160789
dataset_size: 15660398
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
tags:
- math
Simple Math
Just like my teacher gave me homework, i thought maybe we can also add some of these basics on the trainings of our models.
It was created with very simple code that is in the repo, if you add more complex operations and so.. please share the code :D thank you
Does it Works?
34BEAGLES Evaluation:
hf (pretrained=/data/models/UNA-34Beagles-v1-final,dtype=bfloat16,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: auto (8)
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|--------------|-------|------|-----:|--------|-----:|---|-----:|
|arc_challenge |Yaml |none | 25|acc |0.7039|± |0.0133|
| | |none | 25|acc_norm|0.7321|± |0.0129|
|truthfulqa_mc2|Yaml |none | 0|acc |0.7387|± |0.0141|
hf (pretrained=/data/models/UNA-34Beagles-v1-final,dtype=bfloat16,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric |Value | |Stderr|
|-----|-------|----------|-----:|-----------|-----:|---|-----:|
|gsm8k|Yaml |get-answer| 5|exact_match|0.6399|± |0.0132|
| Groups |Version|Filter|n-shot|Metric|Value | |Stderr|
|------------------|-------|------|-----:|------|-----:|---|-----:|
|mmlu |N/A |none | 0|acc |0.7477|± |0.1079|
| - humanities |N/A |none | 0|acc |0.7188|± |0.0855|
| - other |N/A |none | 0|acc |0.7950|± |0.1057|
| - social_sciences|N/A |none | 0|acc |0.8297|± |0.0664|
| - stem |N/A |none | 0|acc |0.6641|± |0.1291|
34BEAGLES-MATH Evaluation
hf (pretrained=/data/models/34BeaglesMath-v1,dtype=bfloat16,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric |Value | |Stderr|
|-----|-------|----------|-----:|-----------|-----:|---|-----:|
|gsm8k|Yaml |get-answer| 5|exact_match|0.6505|± |0.0131|
hf (pretrained=/data/models/34BeaglesMath-v1,dtype=bfloat16,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: auto (8)
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|--------------|-------|------|-----:|--------|-----:|---|-----:|
|arc_challenge |Yaml |none | 25|acc |0.7090|± |0.0133|
| | |none | 25|acc_norm|0.7329|± |0.0129|
|truthfulqa_mc2|Yaml |none | 0|acc |0.7378|± |0.0141|
| Groups |Version|Filter|n-shot|Metric|Value | |Stderr|
|------------------|-------|------|-----:|------|-----:|---|-----:|
|mmlu |N/A |none | 0|acc |0.7524|± |0.1045|
| - humanities |N/A |none | 0|acc |0.7307|± |0.0846|
| - other |N/A |none | 0|acc |0.7937|± |0.1029|
| - social_sciences|N/A |none | 0|acc |0.8274|± |0.0667|
| - stem |N/A |none | 0|acc |0.6708|± |0.1236|
I think it works. Not too difficult, not too easy, as a curriculum works well :)
Note to contributors: thank you to those contributing on the experiment with beautiful commits and good spirit
- The model needs some splits
- The complexity has to be gradual as show in experiments
- Feel free to contribute on the readme Evaluation tests.
- Lets aim to build an ablation & paper together. All contributors will be cited.
- Add your log entry on the version so we can keep a track, thanks.
Versions
24.01.24 Added gradual complexity on a separate script
20-23.01.24 Multiple contributions with operations and increased complexity on the main generator script.
Citations
If you use Simple Math o train your model, please cite on the modelcard or the paper.
@misc{simplemath,
title={Simple-Math: 2+2=4 4-1=3},
author={Xavier Murias},
year={2024},
publisher = {Juanako.AI},
journal = {HuggingFace repository},
howpublished = {\url{https://huggingface.co/datasets/fblgit/simple-math}},
}