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README.md
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---
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tags:
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- generated_from_trainer
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datasets:
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- glue
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metrics:
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- accuracy
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- f1
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model-index:
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- name: baseline-ft-mrpc-IRoberta-b-unquantized
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results:
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- task:
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name: Text Classification
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type: text-classification
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dataset:
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name: glue
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type: glue
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config: mrpc
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split: validation
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args: mrpc
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.8995098039215687
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- name: F1
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type: f1
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value: 0.9266547406082289
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# baseline-ft-mrpc-IRoberta-b-unquantized
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This model is a fine-tuned version of [kssteven/ibert-roberta-base](https://huggingface.co/kssteven/ibert-roberta-base) on the glue dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5354
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- Accuracy: 0.8995
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- F1: 0.9267
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- Combined Score: 0.9131
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 5.0
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
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| 0.1212 | 1.0 | 230 | 0.3401 | 0.8799 | 0.9136 | 0.8967 |
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| 0.0347 | 2.0 | 460 | 0.3085 | 0.8676 | 0.9059 | 0.8868 |
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| 0.0495 | 3.0 | 690 | 0.3552 | 0.8848 | 0.9174 | 0.9011 |
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| 0.0024 | 4.0 | 920 | 0.4960 | 0.8824 | 0.9158 | 0.8991 |
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| 0.0046 | 5.0 | 1150 | 0.5354 | 0.8995 | 0.9267 | 0.9131 |
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### Framework versions
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- Transformers 4.30.2
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- Pytorch 2.0.1+cu118
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- Datasets 2.11.0
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- Tokenizers 0.13.3
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