gbert-large-AmDi-synset-classifier-pair – AI Model by kugler | AlphaNeural AI

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kugler/
gbert-large-AmDi-synset-classifier-pair

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text-classification

generated_from_trainer

deepset/gbert-large

autotrain_compatible

endpoints_compatible

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gbert_synset_classifier_pair

This model is a fine-tuned version of deepset/gbert-large on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.4826
Accuracy: 0.8551
F1: 0.8501
Precision: 0.8590
Recall: 0.8551
F1 Macro: 0.7419
Precision Macro: 0.7334
Recall Macro: 0.7673
F1 Micro: 0.8551
Precision Micro: 0.8551
Recall Micro: 0.8551

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: 20
eval_batch_size: 20
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 80
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 50
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall	F1 Macro	Precision Macro	Recall Macro	F1 Micro	Precision Micro	Recall Micro
2.7743	0.3891	100	1.0270	0.7995	0.7723	0.7757	0.7995	0.5034	0.5166	0.5282	0.7995	0.7995	0.7995
0.8957	0.7782	200	0.6587	0.8291	0.8176	0.8179	0.8291	0.5871	0.5863	0.6064	0.8291	0.8291	0.8291
0.6278	1.1673	300	0.5440	0.8457	0.8363	0.8395	0.8457	0.6407	0.6380	0.6622	0.8457	0.8457	0.8457
0.5132	1.5564	400	0.5295	0.8362	0.8275	0.8355	0.8362	0.6593	0.6525	0.6857	0.8362	0.8362	0.8362
0.4843	1.9455	500	0.4930	0.8511	0.8439	0.8500	0.8511	0.6777	0.6675	0.7028	0.8511	0.8511	0.8511
0.39	2.3346	600	0.4827	0.8564	0.8521	0.8555	0.8564	0.7073	0.6989	0.7287	0.8564	0.8564	0.8564
0.3536	2.7237	700	0.4818	0.8551	0.8492	0.8576	0.8551	0.7314	0.7421	0.7476	0.8551	0.8551	0.8551
0.3462	3.1128	800	0.4826	0.8551	0.8501	0.8590	0.8551	0.7419	0.7334	0.7673	0.8551	0.8551	0.8551

Framework versions

Transformers 4.45.2
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.20.3