bert-base-uncased-finetuned-3d-sentiment – AI Model by venetis | AlphaNeural AI

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venetis/
bert-base-uncased-finetuned-3d-sentiment

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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bert-base-uncased-finetuned-3d-sentiment

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

Loss: 0.9271
Accuracy: 0.7392
Precision: 0.7455
Recall: 0.7392
F1: 0.7394

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: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 6381
num_epochs: 7
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.8443	1.0	1595	0.8265	0.6659	0.6920	0.6659	0.6629
0.6037	2.0	3190	0.7380	0.7021	0.7207	0.7021	0.7014
0.516	3.0	4785	0.6740	0.7246	0.7337	0.7246	0.7234
0.4269	4.0	6380	0.7221	0.7290	0.7383	0.7290	0.7271
0.3149	5.0	7975	0.8368	0.7237	0.7422	0.7237	0.7230
0.1996	6.0	9570	0.9271	0.7392	0.7455	0.7392	0.7394
0.1299	7.0	11165	1.1062	0.7358	0.7461	0.7358	0.7361

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cu117
Datasets 2.10.1
Tokenizers 0.13.3