metadata

license: mit
base_model: xlm-roberta-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: XLM-R-BASE-VanillaFT-5E-spring-feather-1-D-08-03-T-08-15
    results: []

XLM-R-BASE-VanillaFT-5E-spring-feather-1-D-08-03-T-08-15

This model is a fine-tuned version of xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.4196
Precision 0: 0.8318
Precision 1: 0.7503
Recall 0: 0.8119
Recall 1: 0.7767
F1 0: 0.8217
F1 1: 0.7633
Precision Weighted: 0.7987
Recall Weighted: 0.7976
F1 Weighted: 0.7980
Accuracy: 0.7976
F1 Macro: 0.7925

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: 16
eval_batch_size: 16
seed: 402
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision 0	Precision 1	Recall 0	Recall 1	F1 0	F1 1	Precision Weighted	Recall Weighted	F1 Weighted	Accuracy	F1 Macro
0.5481	1.0	469	0.4437	0.7838	0.7441	0.8226	0.6900	0.8028	0.7161	0.7677	0.7688	0.7676	0.7688	0.7594
0.4178	2.0	938	0.4403	0.8529	0.6988	0.7519	0.8211	0.7994	0.7553	0.7904	0.7800	0.7815	0.7800	0.7773
0.3416	3.0	1407	0.4196	0.8318	0.7503	0.8119	0.7767	0.8217	0.7633	0.7987	0.7976	0.7980	0.7976	0.7925
0.2757	4.0	1876	0.4393	0.8430	0.7153	0.7735	0.8024	0.8068	0.7565	0.7912	0.7852	0.7864	0.7852	0.7816
0.2231	5.0	2345	0.4908	0.8343	0.7418	0.8031	0.7827	0.8184	0.7617	0.7968	0.7948	0.7954	0.7948	0.7901

Framework versions

Transformers 4.43.3
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1