Hebrew_affix_find2

leminda-ai/Hebrew_affix_find2

Hebrew_affix_find2 is a machine learning model from leminda-ai. 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 cc-by-4.0.

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

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At a glance

Library: transformers
License: cc-by-4.0
Model type: bert
Access: Public
Created: Sep 2, 2024
Updated: Sep 4, 2024
SHA: 7c4576c2

Base models

dicta-il/dictabert

Model card

From the Hugging Face model README

Hebrew_affix_find2

This model is a fine-tuned version of dicta-il/dictabert on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0534
Precision: 0.4646
Recall: 0.1822
F1-micro: 0.0791
F1-macro: 0.1818
F1 Remove Empty: 0.2674
Accuracy: 0.9087
Precision Complex: 0.2664
Recall Complex: 0.1857
F1-micro Complex: 0.2060
F1-macro Complex: 0.1992
Accuracy Complex: 0.8184

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: 3e-05
train_batch_size: 84
eval_batch_size: 42
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 50
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1-micro	F1-macro	F1 Remove Empty	Accuracy	Precision Complex	Recall Complex	F1-micro Complex	F1-macro Complex	Accuracy Complex
0.2602	0.5995	250	0.2843	0.0729	0.0769	0.0423	0.0711	0.2540	0.9477	0.1411	0.1042	0.1633	0.1149	0.8174
0.1528	1.1990	500	0.1881	0.1228	0.1311	0.0536	0.1034	0.2873	0.9481	0.1637	0.1376	0.1812	0.1467	0.8177
0.1302	1.7986	750	0.1366	0.1447	0.1498	0.0601	0.1109	0.2773	0.9478	0.1548	0.1456	0.1871	0.1451	0.8170
0.0844	2.3981	1000	0.1108	0.1677	0.1572	0.0638	0.1195	0.2716	0.9478	0.1327	0.1471	0.1915	0.1371	0.8173
0.091	2.9976	1250	0.0950	0.1728	0.1911	0.0660	0.1273	0.2653	0.9479	0.1291	0.1568	0.1950	0.1398	0.8172
0.0608	3.5971	1500	0.0819	0.2912	0.1809	0.0688	0.1668	0.2979	0.9485	0.2092	0.1963	0.1999	0.1995	0.8179
0.047	4.1966	1750	0.0740	0.3111	0.2112	0.0695	0.1731	0.2885	0.9484	0.1919	0.1965	0.1997	0.1888	0.8178
0.0421	4.7962	2000	0.0681	0.4088	0.2131	0.0709	0.1804	0.2652	0.9485	0.1875	0.1968	0.2020	0.1875	0.8181
0.0345	5.3957	2250	0.0637	0.4310	0.2131	0.0714	0.1831	0.2692	0.9486	0.2172	0.1963	0.2027	0.2004	0.8183
0.0353	5.9952	2500	0.0598	0.3737	0.2145	0.0719	0.1850	0.2721	0.9486	0.2013	0.1965	0.2032	0.1948	0.8183
0.0291	6.5947	2750	0.0581	0.4234	0.2141	0.0720	0.1844	0.2712	0.9486	0.1943	0.1967	0.2039	0.1907	0.8184
0.024	7.1942	3000	0.0572	0.4579	0.1839	0.0721	0.1846	0.2885	0.9487	0.2161	0.1961	0.2050	0.2018	0.8186
0.0226	7.7938	3250	0.0555	0.4448	0.2041	0.0725	0.1946	0.2861	0.9487	0.2136	0.1963	0.2078	0.2005	0.8189
0.0201	8.3933	3500	0.0558	0.4801	0.2140	0.0725	0.1962	0.2885	0.9487	0.2083	0.1955	0.2057	0.1981	0.8187
0.0192	8.9928	3750	0.0546	0.4622	0.1841	0.0727	0.1913	0.2989	0.9488	0.2285	0.1961	0.2072	0.2065	0.8189
0.0168	9.5923	4000	0.0551	0.4936	0.2038	0.0728	0.2026	0.2980	0.9487	0.2335	0.1963	0.2083	0.2058	0.8190
0.015	10.1918	4250	0.0545	0.4590	0.1944	0.0728	0.1970	0.2897	0.9487	0.2315	0.1963	0.2054	0.2040	0.8186
0.0145	10.7914	4500	0.0559	0.4623	0.1942	0.0730	0.2002	0.2944	0.9488	0.2284	0.1966	0.2093	0.2069	0.8190
0.015	11.3909	4750	0.0578	0.4792	0.1843	0.0730	0.1908	0.2982	0.9488	0.2296	0.1966	0.2081	0.2058	0.8188
0.0146	11.9904	5000	0.0545	0.4733	0.1942	0.0731	0.1978	0.2909	0.9488	0.2088	0.1966	0.2080	0.1956	0.8188
0.0138	12.5899	5250	0.0549	0.4984	0.1940	0.0732	0.1990	0.2927	0.9488	0.2171	0.1966	0.2090	0.1987	0.8189
0.0116	13.1894	5500	0.0555	0.4604	0.1844	0.0733	0.1910	0.2984	0.9488	0.2171	0.1966	0.2086	0.1991	0.8189
0.0122	13.7890	5750	0.0545	0.4992	0.1943	0.0733	0.1985	0.2919	0.9488	0.2156	0.1966	0.2088	0.1998	0.8189
0.0113	14.3885	6000	0.0554	0.5054	0.1943	0.0733	0.2005	0.2948	0.9488	0.2290	0.1966	0.2087	0.2033	0.8189
0.0107	14.9880	6250	0.0556	0.5085	0.1943	0.0734	0.2010	0.2956	0.9488	0.2302	0.1966	0.2092	0.2040	0.8190

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1

Library: transformers
Type: bert
License: cc-by-4.0
Created: Sep 2, 2024
Updated: Sep 4, 2024