---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: baseline-ft-mrpc-IRoberta-b-unquantized
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE MRPC
      type: glue
      config: mrpc
      split: validation
      args: mrpc
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8995098039215687
    - name: F1
      type: f1
      value: 0.9266547406082289
---

<!-- 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. -->

# baseline-ft-mrpc-IRoberta-b-unquantized

This model is a fine-tuned version of [kssteven/ibert-roberta-base](https://huggingface.co/kssteven/ibert-roberta-base) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5354
- Accuracy: 0.8995
- F1: 0.9267
- Combined Score: 0.9131

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.1212        | 1.0   | 230  | 0.3401          | 0.8799   | 0.9136 | 0.8967         |
| 0.0347        | 2.0   | 460  | 0.3085          | 0.8676   | 0.9059 | 0.8868         |
| 0.0495        | 3.0   | 690  | 0.3552          | 0.8848   | 0.9174 | 0.9011         |
| 0.0024        | 4.0   | 920  | 0.4960          | 0.8824   | 0.9158 | 0.8991         |
| 0.0046        | 5.0   | 1150 | 0.5354          | 0.8995   | 0.9267 | 0.9131         |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3