fg-bert-sustainability-2e-5-0.01-32-20_augmented_60_percent_empty_2 – AI Model by yacine-djm | AlphaNeural AI

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yacine-djm/
fg-bert-sustainability-2e-5-0.01-32-20_augmented_60_percent_empty_2

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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fg-bert-sustainability-2e-5-0.01-32-20_augmented_60_percent_empty_2

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

Loss: 0.0304
F1: 0.9166
Roc Auc: 0.9580
Accuracy: 0.9460

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: 2.5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
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	F1	Roc Auc	Accuracy
No log	1.0	63	0.1679	0.0	0.5	0.5991
No log	1.99	126	0.0858	0.6802	0.7690	0.8048
No log	2.99	189	0.0525	0.8974	0.9423	0.9361
No log	4.0	253	0.0415	0.9041	0.9470	0.9395
No log	5.0	316	0.0381	0.9023	0.9479	0.9381
No log	5.99	379	0.0345	0.9082	0.9466	0.9420
No log	6.99	442	0.0321	0.9155	0.9546	0.9465
0.0888	8.0	506	0.0319	0.9106	0.9560	0.9415
0.0888	9.0	569	0.0313	0.9123	0.9572	0.9420
0.0888	9.96	630	0.0304	0.9166	0.9580	0.9460

Framework versions

Transformers 4.30.2
Pytorch 2.0.0
Datasets 2.1.0
Tokenizers 0.13.3