README.md

metadata

library_name: transformers
license: apache-2.0
base_model: google/rembert
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: populism_classifier_410
    results: []

populism_classifier_410

This model is a fine-tuned version of google/rembert on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.7024
• Accuracy: 0.1263
• 1-f1: 0.2242
• 1-recall: 1.0
• 1-precision: 0.1263
• Balanced Acc: 0.5

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: 1e-05
• train_batch_size: 16
• eval_batch_size: 32
• 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: 0.06
• num_epochs: 20
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	1-f1	1-recall	1-precision	Balanced Acc
0.6697	1.0	50	0.6939	0.1263	0.2242	1.0	0.1263	0.5
0.6672	2.0	100	0.7424	0.1263	0.2242	1.0	0.1263	0.5
0.8553	3.0	150	0.8350	0.1263	0.2242	1.0	0.1263	0.5
0.6019	4.0	200	0.7917	0.1263	0.2242	1.0	0.1263	0.5
0.5024	5.0	250	0.6939	0.1414	0.2273	1.0	0.1282	0.5087
0.6982	6.0	300	0.7261	0.1364	0.2262	1.0	0.1276	0.5058
0.8583	7.0	350	0.7103	0.1263	0.2242	1.0	0.1263	0.5
0.5487	8.0	400	0.7149	0.1364	0.2262	1.0	0.1276	0.5058
0.5309	9.0	450	0.7218	0.1313	0.2252	1.0	0.1269	0.5029
0.5436	10.0	500	0.6868	0.1919	0.2381	1.0	0.1351	0.5376
0.5617	11.0	550	0.6876	0.1919	0.2381	1.0	0.1351	0.5376
0.8479	12.0	600	0.6812	0.1919	0.2381	1.0	0.1351	0.5376
1.4176	13.0	650	0.6805	0.1818	0.2358	1.0	0.1337	0.5318
0.3742	14.0	700	0.6920	0.1566	0.2304	1.0	0.1302	0.5173
0.7671	15.0	750	0.6905	0.1465	0.2283	1.0	0.1289	0.5116
0.994	16.0	800	0.6967	0.1313	0.2252	1.0	0.1269	0.5029
0.3561	17.0	850	0.6962	0.1414	0.2273	1.0	0.1282	0.5087
0.5991	18.0	900	0.7024	0.1263	0.2242	1.0	0.1263	0.5

Framework versions

• Transformers 4.46.3
• Pytorch 2.4.1+cu121
• Datasets 3.1.0
• Tokenizers 0.20.3