SloBertAA_Top5_WithoutOOC_082023 – AI Model by gregorgabrovsek | AlphaNeural AI

You can deploy this model and start earning money today!

gregorgabrovsek/
SloBertAA_Top5_WithoutOOC_082023

like

0

text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

Views

No views yet

SloBertAA_Top5_WithoutOOC_082023

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

Loss: 0.4398
Accuracy: 0.9478
F1: 0.9478
Precision: 0.9481
Recall: 0.9478

Other related models

Models fine-tuned on the RTV datasets:

Base model	Includes the OOC class?	5 classes	10 classes	20 classes	50 classes	100 classes
SloBERTa	Yes	link	link	link	link	link
SloBERTa	No	link	link	link	link	link
BERT Multilingual	Yes	link	link	link	link	link
BERT Multilingual	No	link	link	link	link	link

Models fine-tuned on the IMDb datasets:

Base model	Includes the OOC class?	5 classes	10 classes	25 classes	50 classes	100 classes
BERT Multilingual	No	link	link	link	link	link

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: 12
eval_batch_size: 12
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	Accuracy	F1	Precision	Recall
0.2468	1.0	8757	0.2629	0.9194	0.9199	0.9226	0.9194
0.1864	2.0	17514	0.2340	0.9375	0.9377	0.9386	0.9375
0.1419	3.0	26271	0.2821	0.9371	0.9374	0.9389	0.9371
0.1001	4.0	35028	0.3135	0.9408	0.9409	0.9412	0.9408
0.0727	5.0	43785	0.3584	0.9415	0.9416	0.9423	0.9415
0.057	6.0	52542	0.3552	0.9440	0.9442	0.9450	0.9440
0.0332	7.0	61299	0.4318	0.9394	0.9396	0.9407	0.9394
0.0268	8.0	70056	0.4376	0.9442	0.9444	0.9455	0.9442
0.019	9.0	78813	0.4377	0.9444	0.9445	0.9451	0.9444
0.0181	10.0	87570	0.4398	0.9478	0.9478	0.9481	0.9478

Framework versions

Transformers 4.26.1
Pytorch 1.8.0
Datasets 2.10.1
Tokenizers 0.13.2