ABSA-OTE-domainAdapt-SemEval-Adapter-pfeiffer_madx-run1

salohnana2018/ABSA-OTE-domainAdapt-SemEval-Adapter-pfeiffer_madx-run1

ABSA-OTE-domainAdapt-SemEval-Adapter-pfeiffer_madx-run1 is a machine learning model from salohnana2018. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: transformers
License: apache-2.0
Model type: bert
Access: Public
Created: Jul 8, 2023
Updated: Jul 9, 2023
SHA: ab05f654

Model card

From the Hugging Face model README

ABSA-OTE-domainAdapt-SemEval-Adapter-pfeiffer_madx-run1

This model is a fine-tuned version of CAMeL-Lab/bert-base-arabic-camelbert-msa on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1191
Precision: 0.7498
Recall: 0.7778
F1: 0.7635
Accuracy: 0.9547

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: 0.0001
train_batch_size: 32
eval_batch_size: 8
seed: 25
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.275	1.0	121	0.1425	0.7125	0.6791	0.6954	0.9472
0.1317	2.0	242	0.1232	0.7679	0.7170	0.7416	0.9521
0.1194	3.0	363	0.1197	0.7629	0.7359	0.7492	0.9529
0.114	4.0	484	0.1178	0.7651	0.7434	0.7541	0.9538
0.1104	5.0	605	0.1168	0.7790	0.7270	0.7521	0.9545
0.1045	6.0	726	0.1163	0.7499	0.7529	0.7514	0.9533
0.1024	7.0	847	0.1171	0.7369	0.7688	0.7525	0.9528
0.0996	8.0	968	0.1151	0.7585	0.7544	0.7564	0.9545
0.0978	9.0	1089	0.1179	0.7262	0.7902	0.7569	0.9522
0.0952	10.0	1210	0.1161	0.7782	0.7519	0.7648	0.9562
0.095	11.0	1331	0.1153	0.7752	0.7524	0.7636	0.9570
0.0902	12.0	1452	0.1194	0.7499	0.7678	0.7587	0.9541
0.0918	13.0	1573	0.1170	0.7600	0.7683	0.7641	0.9555
0.0884	14.0	1694	0.1183	0.7344	0.7922	0.7622	0.9539
0.0866	15.0	1815	0.1178	0.7819	0.7519	0.7666	0.9566
0.0852	16.0	1936	0.1188	0.7486	0.7788	0.7634	0.9548
0.0856	17.0	2057	0.1185	0.7586	0.7703	0.7644	0.9554
0.084	18.0	2178	0.1193	0.7462	0.7793	0.7624	0.9543
0.0837	19.0	2299	0.1193	0.7533	0.7728	0.7629	0.9545
0.0833	20.0	2420	0.1191	0.7498	0.7778	0.7635	0.9547

Framework versions

Transformers 4.26.1
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3

Library: transformers
Type: bert
License: apache-2.0
Created: Jul 8, 2023
Updated: Jul 9, 2023