albert-base-v2-grammar-ner

codymd/albert-base-v2-grammar-ner

albert-base-v2-grammar-ner is a token classification model from codymd. Use it when you need labels on individual words, such as names. 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

Task: Token Classification
Library: transformers
License: apache-2.0
Model type: albert
Access: Public
Created: Dec 12, 2024
Updated: Dec 12, 2024
SHA: bf630bea

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Base models

albert/albert-base-v2

Model card

From the Hugging Face model README

albert-base-v2-grammar-ner

This model is a fine-tuned version of albert/albert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1134
Accuracy: 0.9870
F1 Macro: 0.7941
F1 Micro: 0.9008
Precision Macro: 0.8789
Precision Micro: 0.9569
Recall Macro: 0.7518
Recall Micro: 0.8510

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: 5e-05
train_batch_size: 24
eval_batch_size: 24
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 18

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	F1 Micro	Precision Macro	Precision Micro	Recall Macro	Recall Micro
0.4297	1.0	93	0.2896	0.9313	0.1318	0.4462	0.1897	0.5163	0.1281	0.3928
0.2521	2.0	186	0.2192	0.9452	0.2315	0.5160	0.3282	0.6752	0.1962	0.4176
0.167	3.0	279	0.1630	0.9662	0.3546	0.7198	0.4142	0.8358	0.3295	0.6321
0.1026	4.0	372	0.1343	0.9733	0.4185	0.7769	0.5241	0.8732	0.3797	0.6998
0.0718	5.0	465	0.1231	0.9738	0.4644	0.7794	0.5584	0.8525	0.4382	0.7178
0.0483	6.0	558	0.1269	0.9778	0.4778	0.8204	0.6262	0.9415	0.4164	0.7269
0.0335	7.0	651	0.1162	0.9804	0.6028	0.8416	0.6985	0.8834	0.5846	0.8036
0.0233	8.0	744	0.1203	0.9813	0.5736	0.8475	0.7429	0.9496	0.4988	0.7652
0.0171	9.0	837	0.1052	0.9836	0.6502	0.8671	0.7023	0.8964	0.6490	0.8397
0.01	10.0	930	0.1125	0.9805	0.6681	0.8477	0.6854	0.8535	0.6875	0.8420
0.0084	11.0	1023	0.1058	0.9862	0.7195	0.8894	0.8004	0.9287	0.6870	0.8533
0.0051	12.0	1116	0.1092	0.9870	0.8015	0.9015	0.8810	0.95	0.7612	0.8578
0.0031	13.0	1209	0.1131	0.9865	0.8006	0.8983	0.8827	0.9429	0.7592	0.8578
0.0017	14.0	1302	0.1106	0.9873	0.8058	0.9039	0.8748	0.9525	0.7749	0.8600
0.0012	15.0	1395	0.1111	0.9875	0.7985	0.9058	0.8818	0.9596	0.7576	0.8578
0.0009	16.0	1488	0.1128	0.9870	0.7941	0.9008	0.8789	0.9569	0.7518	0.8510
0.0008	17.0	1581	0.1133	0.9870	0.7941	0.9008	0.8789	0.9569	0.7518	0.8510
0.0008	18.0	1674	0.1134	0.9870	0.7941	0.9008	0.8789	0.9569	0.7518	0.8510

Framework versions

Transformers 4.46.3
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.20.3

Task: Token Classification
Library: transformers
Type: albert
License: apache-2.0
Created: Dec 12, 2024
Updated: Dec 12, 2024