---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
datasets:
- super_glue
metrics:
- accuracy
- f1
model-index:
- name: roberta-multirc
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: super_glue
      type: super_glue
      config: multirc
      split: validation
      args: multirc
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.5738448844884488
    - name: F1
      type: f1
      value: 0.43142386224389884
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# roberta-multirc

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the super_glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6811
- Accuracy: 0.5738
- F1: 0.4314

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use 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_ratio: 0.1
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6872        | 1.0   | 1703 | 0.6811          | 0.5738   | 0.4314 |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3