AnonymousCS/populism_classifier_bsample_080

1---2library_name: transformers3license: mit4base_model: FacebookAI/xlm-roberta-base5tags:6- generated_from_trainer7metrics:8- accuracy9model-index:10- name: populism_classifier_bsample_08011  results: []12---13 14<!-- This model card has been generated automatically according to the information the Trainer had access to. You15should probably proofread and complete it, then remove this comment. -->16 17# populism_classifier_bsample_08018 19This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.20It achieves the following results on the evaluation set:21- Loss: 0.774122- Accuracy: 0.816423- 1-f1: 0.222224- 1-recall: 0.888925- 1-precision: 0.127026- Balanced Acc: 0.851527 28## Model description29 30More information needed31 32## Intended uses & limitations33 34More information needed35 36## Training and evaluation data37 38More information needed39 40## Training procedure41 42### Training hyperparameters43 44The following hyperparameters were used during training:45- learning_rate: 3e-0546- train_batch_size: 1647- eval_batch_size: 1648- seed: 4249- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments50- lr_scheduler_type: linear51- num_epochs: 1552- mixed_precision_training: Native AMP53 54### Training results55 56| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1   | 1-recall | 1-precision | Balanced Acc |57|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|58| 0.0652        | 1.0   | 8    | 0.6031          | 0.8459   | 0.2295 | 0.7778   | 0.1346      | 0.8129       |59| 0.0156        | 2.0   | 16   | 1.9513          | 0.6164   | 0.1333 | 1.0      | 0.0714      | 0.8024       |60| 0.0343        | 3.0   | 24   | 0.7741          | 0.8164   | 0.2222 | 0.8889   | 0.1270      | 0.8515       |61 62 63### Framework versions64 65- Transformers 4.46.366- Pytorch 2.4.1+cu12167- Datasets 3.1.068- Tokenizers 0.20.369