---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: causalbench_math-roberta-base
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# causalbench_math-roberta-base

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2645
- Accuracy: 0.8953
- Macro F1: 0.7952
- Micro F1: 0.8953

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 | Micro F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
| No log        | 1.0   | 162  | 0.4244          | 0.8357   | 0.4576   | 0.8357   |
| No log        | 2.0   | 324  | 0.2566          | 0.8899   | 0.7933   | 0.8899   |
| No log        | 3.0   | 486  | 0.2645          | 0.8953   | 0.7952   | 0.8953   |


### Framework versions

- Transformers 4.50.3
- Pytorch 2.11.0+cu130
- Datasets 3.6.0
- Tokenizers 0.21.4