gbert-large-topic_classification – AI Model by izaitova | AlphaNeural AI

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izaitova/
gbert-large-topic_classification

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text-classification

generated_from_trainer

deepset/gbert-large

autotrain_compatible

endpoints_compatible

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gbert-large-topic_classification

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: 0.5093
Precision: 0.9100
Recall: 0.8993
F1: 0.9042
Accuracy: 0.9167

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: 5e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	44	0.6465	0.8897	0.8138	0.8179	0.8480
No log	2.0	88	0.2949	0.9116	0.9110	0.9106	0.9118
No log	3.0	132	0.4110	0.9298	0.8939	0.9020	0.9167
No log	4.0	176	0.6242	0.9261	0.8756	0.8911	0.9020
No log	5.0	220	0.5606	0.9208	0.8757	0.8897	0.9020
No log	6.0	264	0.6164	0.9201	0.8867	0.9001	0.9069
No log	7.0	308	0.4898	0.9155	0.9001	0.9071	0.9167
No log	8.0	352	0.4999	0.9191	0.9029	0.9102	0.9216
No log	9.0	396	0.5073	0.9100	0.8993	0.9042	0.9167
No log	10.0	440	0.5093	0.9100	0.8993	0.9042	0.9167

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1