bert-base-cased-ner_cv-med-ft – AI Model by jhonparra18 | AlphaNeural AI

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jhonparra18/
bert-base-cased-ner_cv-med-ft

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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bert-base-cased-ner_cv-med-ft

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

Loss: 0.5926
Precision: 0.2559
Recall: 0.3460
F1: 0.2942
Accuracy: 0.8368

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: 1e-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
lr_scheduler_warmup_steps: 10
num_epochs: 30
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
1.9616	2.73	30	0.7717	0.0	0.0	0.0	0.8608
0.9266	5.45	60	0.6687	0.0	0.0	0.0	0.8608
0.8486	8.18	90	0.6100	0.2133	0.0488	0.0794	0.8635
0.7421	10.91	120	0.5922	0.2534	0.1966	0.2215	0.8542
0.6481	13.64	150	0.5696	0.2889	0.2378	0.2609	0.8596
0.5948	16.36	180	0.5798	0.2678	0.3034	0.2845	0.8472
0.5621	19.09	210	0.5913	0.2486	0.3293	0.2833	0.8381
0.5234	21.82	240	0.5816	0.2585	0.3262	0.2884	0.8404
0.5028	24.55	270	0.5944	0.2545	0.3476	0.2938	0.8368
0.4975	27.27	300	0.5923	0.2531	0.3476	0.2929	0.8368
0.4791	30.0	330	0.5926	0.2559	0.3460	0.2942	0.8368

Framework versions

Transformers 4.21.1
Pytorch 1.8.1+cu111
Datasets 1.6.2
Tokenizers 0.12.1