multiCorp_5e-05_LabelNorm_0404

Brizape/multiCorp_5e-05_LabelNorm_0404

multiCorp_5e-05_LabelNorm_0404 is a token classification model from Brizape. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.

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

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At a glance

Task: Token Classification
Library: transformers
License: mit
Model type: bert
Access: Public
Created: Apr 5, 2023
Updated: Apr 5, 2023
SHA: 5859e892

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Model card

From the Hugging Face model README

multiCorp_5e-05_LabelNorm_0404

This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0310
Precision: 0.6947
Recall: 0.7305
F1: 0.7121
Accuracy: 0.9911

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 2000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.2337	0.08	25	0.1020	0.0	0.0	0.0	0.9838
0.0855	0.15	50	0.1026	0.0	0.0	0.0	0.9838
0.0834	0.23	75	0.1037	0.0	0.0	0.0	0.9838
0.0868	0.31	100	0.0682	0.0	0.0	0.0	0.9838
0.0569	0.39	125	0.0630	0.0	0.0	0.0	0.9838
0.0612	0.46	150	0.0595	0.0	0.0	0.0	0.9838
0.0551	0.54	175	0.0507	0.3790	0.3046	0.3378	0.9841
0.0347	0.62	200	0.0504	0.5773	0.2403	0.3393	0.9859
0.0472	0.7	225	0.0474	0.5132	0.3795	0.4363	0.9862
0.0374	0.77	250	0.0444	0.5509	0.3039	0.3917	0.9857
0.0525	0.85	275	0.0383	0.6984	0.4281	0.5309	0.9882
0.0406	0.93	300	0.0389	0.6280	0.5105	0.5632	0.9885
0.0347	1.01	325	0.0404	0.5134	0.6003	0.5535	0.9873
0.0333	1.08	350	0.0415	0.6409	0.4289	0.5139	0.9880
0.0267	1.16	375	0.0377	0.6010	0.4543	0.5175	0.9885
0.0406	1.24	400	0.0397	0.6406	0.6310	0.6357	0.9889
0.0252	1.32	425	0.0364	0.5946	0.5599	0.5767	0.9882
0.0351	1.39	450	0.0343	0.6632	0.6130	0.6371	0.9895
0.0304	1.47	475	0.0378	0.6756	0.5846	0.6268	0.9896
0.0242	1.55	500	0.0342	0.7061	0.5988	0.6480	0.9904
0.0291	1.63	525	0.0327	0.5841	0.6497	0.6152	0.9894
0.0307	1.7	550	0.0333	0.5815	0.5823	0.5819	0.9890
0.0278	1.78	575	0.0316	0.6654	0.6579	0.6616	0.9904
0.0293	1.86	600	0.0335	0.7969	0.4933	0.6093	0.9898
0.0241	1.93	625	0.0322	0.6473	0.6991	0.6722	0.9901
0.0288	2.01	650	0.0300	0.7431	0.5846	0.6544	0.9906
0.0192	2.09	675	0.0329	0.6908	0.7006	0.6957	0.9910
0.0184	2.17	700	0.0326	0.6788	0.6407	0.6592	0.9897
0.0171	2.24	725	0.0325	0.7131	0.6864	0.6995	0.9911
0.0161	2.32	750	0.0336	0.7138	0.6647	0.6884	0.9910
0.0218	2.4	775	0.0306	0.6892	0.6954	0.6923	0.9904
0.0209	2.48	800	0.0310	0.6947	0.7305	0.7121	0.9911

Framework versions

Transformers 4.27.4
Pytorch 2.0.0+cu118
Datasets 2.11.0
Tokenizers 0.13.3

Task: Token Classification
Library: transformers
Type: bert
License: mit
Created: Apr 5, 2023
Updated: Apr 5, 2023