---
library_name: transformers
license: mit
base_model: FacebookAI/roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-continual-kennedy2020constructing-S0T1
  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. -->

# roberta-base-continual-kennedy2020constructing-S0T1

This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5398
- Accuracy: 0.5667
- Roc Auc: 0.7793
- Micro Precision: 0.5667
- Macro Precision: 0.5834
- Weighted Precision: 0.5834
- Micro Recall: 0.5667
- Macro Recall: 0.5667
- Weighted Recall: 0.5667
- Micro F1: 0.5667
- Macro F1: 0.5568
- Weighted F1: 0.5568

## 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: 32
- eval_batch_size: 64
- 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Roc Auc | Micro Precision | Macro Precision | Weighted Precision | Micro Recall | Macro Recall | Weighted Recall | Micro F1 | Macro F1 | Weighted F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------:|:---------------:|:---------------:|:------------------:|:------------:|:------------:|:---------------:|:--------:|:--------:|:-----------:|
| No log        | 1.0   | 57   | 1.3397          | 0.3333   | 0.6652  | 0.3333          | 0.1111          | 0.1111             | 0.3333       | 0.3333       | 0.3333          | 0.3333   | 0.1667   | 0.1667      |
| No log        | 2.0   | 114  | 1.0461          | 0.48     | 0.7563  | 0.48            | 0.3508          | 0.3508             | 0.48         | 0.48         | 0.48            | 0.48     | 0.3810   | 0.3810      |
| No log        | 3.0   | 171  | 1.0020          | 0.5533   | 0.7707  | 0.5533          | 0.4876          | 0.4876             | 0.5533       | 0.5533       | 0.5533          | 0.5533   | 0.4667   | 0.4667      |
| No log        | 4.0   | 228  | 1.1591          | 0.5133   | 0.7707  | 0.5133          | 0.5582          | 0.5582             | 0.5133       | 0.5133       | 0.5133          | 0.5133   | 0.4790   | 0.4790      |
| No log        | 5.0   | 285  | 1.2874          | 0.5467   | 0.7651  | 0.5467          | 0.5471          | 0.5471             | 0.5467       | 0.5467       | 0.5467          | 0.5467   | 0.5278   | 0.5278      |
| No log        | 6.0   | 342  | 1.1093          | 0.6067   | 0.7936  | 0.6067          | 0.5863          | 0.5863             | 0.6067       | 0.6067       | 0.6067          | 0.6067   | 0.5874   | 0.5874      |
| No log        | 7.0   | 399  | 1.2420          | 0.6      | 0.7948  | 0.6             | 0.5925          | 0.5925             | 0.6          | 0.6          | 0.6             | 0.6      | 0.5860   | 0.5860      |
| No log        | 8.0   | 456  | 1.5520          | 0.5467   | 0.7737  | 0.5467          | 0.5457          | 0.5457             | 0.5467       | 0.5467       | 0.5467          | 0.5467   | 0.5282   | 0.5282      |
| 0.4501        | 9.0   | 513  | 1.5398          | 0.5667   | 0.7793  | 0.5667          | 0.5834          | 0.5834             | 0.5667       | 0.5667       | 0.5667          | 0.5667   | 0.5568   | 0.5568      |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0