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library_name: peft
license: mit
base_model: roberta-base
tags:
- base_model:adapter:roberta-base
- lora
- transformers
metrics:
- accuracy
- matthews_correlation
- f1
- precision
- recall
model-index:
- name: peft-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. -->
# peft-roberta-base
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0933
- Accuracy: 0.9745
- Matthews Correlation: 0.9662
- F1: 0.9609
- Precision: 0.9550
- Recall: 0.9671
## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Matthews Correlation | F1 | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------------------:|:------:|:---------:|:------:|
| 1.1892 | 0.1977 | 1400 | 0.2318 | 0.9216 | 0.8977 | 0.8968 | 0.8726 | 0.9270 |
| 0.6786 | 0.3954 | 2800 | 0.1395 | 0.9621 | 0.9497 | 0.9438 | 0.9332 | 0.9567 |
| 0.6029 | 0.5931 | 4200 | 0.1098 | 0.9696 | 0.9597 | 0.9558 | 0.9491 | 0.9629 |
| 0.5632 | 0.7908 | 5600 | 0.0951 | 0.9742 | 0.9658 | 0.9602 | 0.9539 | 0.9672 |
| 0.5216 | 0.9885 | 7000 | 0.0933 | 0.9745 | 0.9662 | 0.9609 | 0.9550 | 0.9671 |
### Framework versions
- PEFT 0.18.1
- Transformers 5.2.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2 |