fator-argument-quality – AI Model by FareehaAly | AlphaNeural AI

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FareehaAly/
fator-argument-quality

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text-classification

generated_from_trainer

distilbert/distilbert-base-uncased

text-embeddings-inference

endpoints_compatible

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fator-argument-quality

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.5797
Accuracy: 0.8014
F1 Macro: 0.5806
F1 Weighted: 0.7459

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: 32
eval_batch_size: 64
seed: 42
optimizer: Use OptimizerNames.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: 0.1
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	F1 Weighted
0.5548	1.0	558	0.5412	0.7741	0.3669	0.7188
0.4892	2.0	1116	0.5830	0.7866	0.3644	0.7055
0.4455	3.0	1674	0.5797	0.7814	0.4606	0.7449
0.3561	4.0	2232	0.6330	0.7902	0.4996	0.7457
0.3084	5.0	2790	0.6744	0.8057	0.5806	0.7459

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2