results_soft_label – AI Model by ilyaschaki-numrah | AlphaNeural AI

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ilyaschaki-numrah/
results_soft_label

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text-classification

generated_from_trainer

FacebookAI/xlm-roberta-large

text-embeddings-inference

endpoints_compatible

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results_soft_label

This model is a fine-tuned version of xlm-roberta-large on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.2341
Mae: 0.0495
Rmse: 0.1321
Pearson Correlation: 0.9561
Auc Roc: 0.9866
Average Precision: 0.9803
F1 At 0.5: 0.8754
Precision At 0.5: 0.7871
Recall At 0.5: 0.9862

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: 64
seed: 42
optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Mae	Rmse	Pearson Correlation	Auc Roc	Average Precision	F1 At 0.5	Precision At 0.5	Recall At 0.5
0.2484	1.0	11243	0.2585	0.0659	0.1608	0.9350	0.9802	0.9735	0.8617	0.7706	0.9772
0.2275	2.0	22486	0.2410	0.0541	0.1404	0.9506	0.9852	0.9800	0.8710	0.7811	0.9842
0.228	3.0	33729	0.2341	0.0495	0.1321	0.9561	0.9866	0.9803	0.8754	0.7871	0.9862

Framework versions

Transformers 4.57.1
Pytorch 2.9.0+cu128
Datasets 4.3.0
Tokenizers 0.22.1