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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- hendrycks/competition_math
widget:
- text: Find the number of positive divisors of 9!.
example_title: Number theory
- text: Quadrilateral $ABCD$ is a parallelogram. If the measure of angle $A$ is 62
degrees and the measure of angle $ADB$ is 75 degrees, what is the measure of angle
$ADC$, in degrees?
example_title: Prealgebra
- text: Suppose $x \in [-5,-3]$ and $y \in [2,4]$. What is the largest possible value
of $\frac{x+y}{x-y}$?
example_title: Intermediate algebra
base_model: bert-base-uncased
model-index:
- name: bert-finetuned-math-prob-classification
results: []
---
# bert-finetuned-math-prob-classification
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the part of the [competition_math dataset](https://huggingface.co/datasets/competition_math). Specifically, it was trained as a multi-class multi-label model on the problem text. The problem types (labels) used here are "Counting & Probability", "Prealgebra", "Algebra", "Number Theory", "Geometry", "Intermediate Algebra", and "Precalculus".
## Model description
See the [bert-base-uncased](https://huggingface.co/bert-base-uncased) model for more details. The only architectural modification made was to the classification head. Here, 7 classes were used.
## Intended uses & limitations
This model is intended for demonstration purposes only. The problem type data was in English and contains many LaTeX tokens.
## Training and evaluation data
The `problem` field of [competition_math dataset](https://huggingface.co/datasets/competition_math) was used for training and evaluation input data. The target data was taken from the `type` field.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
This fine-tuned model achieves the following result on the problem type competition math test set:
```
precision recall f1-score support
Algebra 0.78 0.79 0.79 1187
Counting & Probability 0.75 0.81 0.78 474
Geometry 0.76 0.83 0.79 479
Intermediate Algebra 0.86 0.84 0.85 903
Number Theory 0.79 0.82 0.80 540
Prealgebra 0.66 0.61 0.63 871
Precalculus 0.95 0.89 0.92 546
accuracy 0.79 5000
macro avg 0.79 0.80 0.79 5000
weighted avg 0.79 0.79 0.79 5000
```
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
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