xlm_r_base-finetuned_after_mrp-v2-ruby-galaxy-10 – AI Model by haturusinghe | AlphaNeural AI

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haturusinghe/
xlm_r_base-finetuned_after_mrp-v2-ruby-galaxy-10

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text-classification

generated_from_trainer

FacebookAI/xlm-roberta-base

autotrain_compatible

endpoints_compatible

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xlm_r_base-finetuned_after_mrp-v2-ruby-galaxy-10

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: 0.4889
Precision 0: 0.8751
Precision 1: 0.8092
Recall 0: 0.8680
Recall 1: 0.8187
F1 0: 0.8715
F1 1: 0.8139
Precision Weighted: 0.8483
Recall Weighted: 0.848
F1 Weighted: 0.8481
F1 Macro: 0.8427

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: 42
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	F1 Macro
0.5138	1.0	469	0.4262	0.8537	0.7847	0.8525	0.7862	0.8531	0.7854	0.8257	0.8256	0.8256	0.8193
0.3695	2.0	938	0.4167	0.9012	0.7257	0.7737	0.8759	0.8326	0.7938	0.8299	0.8152	0.8168	0.8132
0.3741	3.0	1407	0.3583	0.8823	0.7741	0.8330	0.8374	0.8569	0.8045	0.8384	0.8348	0.8357	0.8307
0.2552	4.0	1876	0.4342	0.8708	0.7965	0.8579	0.8138	0.8643	0.8051	0.8407	0.84	0.8403	0.8347
0.2013	5.0	2345	0.4889	0.8751	0.8092	0.8680	0.8187	0.8715	0.8139	0.8483	0.848	0.8481	0.8427

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.19.2
Tokenizers 0.19.1