bert-base-multilingual-cased-finetuned-multilingual_pos

MayaGalvez/bert-base-multilingual-cased-finetuned-multilingual_pos

bert-base-multilingual-cased-finetuned-multilingual_pos is a token classification model from MayaGalvez. 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: bert
Access: Public
Created: Sep 15, 2022
Updated: Sep 15, 2022
SHA: 86cbec4b

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Model card

From the Hugging Face model README

bert-base-multilingual-cased-finetuned-multilingual-pos

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

Loss: 0.1999
Precision: 0.9438
Recall: 0.9438
F1: 0.9438
Accuracy: 0.9541

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: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 7

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
1.0385	0.29	100	0.4411	0.8523	0.8473	0.8498	0.8739
0.3849	0.57	200	0.3275	0.8907	0.8913	0.8910	0.9103
0.2976	0.86	300	0.2879	0.9034	0.9037	0.9036	0.9203
0.2487	1.14	400	0.2599	0.9132	0.9115	0.9123	0.9285
0.2027	1.43	500	0.2444	0.9224	0.9198	0.9211	0.9349
0.1899	1.71	600	0.2287	0.9239	0.9246	0.9243	0.9378
0.18	2.0	700	0.2184	0.9282	0.9297	0.9289	0.9418
0.1351	2.29	800	0.2214	0.9297	0.9291	0.9294	0.9424
0.134	2.57	900	0.2123	0.9337	0.9333	0.9335	0.9458
0.1294	2.86	1000	0.1993	0.9359	0.9344	0.9352	0.9476
0.1156	3.14	1100	0.2018	0.9377	0.9377	0.9377	0.9494
0.1007	3.43	1200	0.2027	0.9375	0.9384	0.9380	0.9495
0.0959	3.71	1300	0.1971	0.9387	0.9394	0.9390	0.9505
0.0982	4.0	1400	0.1953	0.9408	0.9414	0.9411	0.9522
0.0761	4.29	1500	0.1987	0.9404	0.9412	0.9408	0.9517
0.0788	4.57	1600	0.1994	0.9405	0.9411	0.9408	0.9518
0.0755	4.86	1700	0.2009	0.9413	0.9420	0.9417	0.9525
0.0671	5.14	1800	0.2011	0.9421	0.9423	0.9422	0.9527
0.0636	5.43	1900	0.2002	0.9428	0.9431	0.9430	0.9532
0.0628	5.71	2000	0.1993	0.9422	0.9433	0.9428	0.9532
0.0645	6.0	2100	0.1979	0.9434	0.9430	0.9432	0.9536
0.0543	6.29	2200	0.2017	0.9427	0.9434	0.9430	0.9532
0.0558	6.57	2300	0.1992	0.9427	0.9432	0.9430	0.9534
0.0529	6.86	2400	0.1999	0.9438	0.9438	0.9438	0.9541

Framework versions

Transformers 4.21.0
Pytorch 1.12.0+cu102
Datasets 2.4.0
Tokenizers 0.12.1

Task: Token Classification
Library: transformers
Type: bert
License: apache-2.0
Created: Sep 15, 2022
Updated: Sep 15, 2022