checkpoints – AI Model by abk20031218 | AlphaNeural AI

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abk20031218/
checkpoints

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token-classification

generated_from_trainer

facebook/esm2_t12_35M_UR50D

endpoints_compatible

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checkpoints

This model is a fine-tuned version of facebook/esm2_t12_35M_UR50D on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.6773
Accuracy: 0.7253
Precision: 0.3633
Recall: 0.4923
F1: 0.4180

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: 8
eval_batch_size: 8
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
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.6281	1.0	1028	0.6312	0.7622	0.4035	0.3903	0.3968
0.5836	2.0	2056	0.6297	0.7553	0.3973	0.4273	0.4118
0.5832	3.0	3084	0.6605	0.7754	0.4326	0.3880	0.4091
0.5177	4.0	4112	0.6891	0.7604	0.4052	0.4179	0.4114
0.5016	5.0	5140	0.6773	0.7253	0.3633	0.4923	0.4180

Framework versions

Transformers 5.13.1
Pytorch 2.11.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2