NLP-CIC-WFU_Clinical_Cases_NER_mBERT_cased_fine_tuned – AI Model by ajtamayoh | AlphaNeural AI

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NLP-CIC-WFU_Clinical_Cases_NER_mBERT_cased_fine_tuned

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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NLP-CIC-WFU_Clinical_Cases_NER_mBERT_cased_fine_tuned

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: 0.0501
Precision: 0.8961
Recall: 0.7009
F1: 0.7865
Accuracy: 0.9898

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: 8
eval_batch_size: 8
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
No log	1.0	94	0.0484	0.9002	0.6340	0.7440	0.9876
No log	2.0	188	0.0436	0.9095	0.6599	0.7649	0.9887
No log	3.0	282	0.0462	0.8545	0.7043	0.7722	0.9883
No log	4.0	376	0.0456	0.9058	0.6761	0.7743	0.9894
No log	5.0	470	0.0447	0.9194	0.6836	0.7841	0.9900
0.0426	6.0	564	0.0480	0.8917	0.7026	0.7859	0.9897
0.0426	7.0	658	0.0501	0.8961	0.7009	0.7865	0.9898

Framework versions

Transformers 4.19.2
Pytorch 1.11.0+cu113
Datasets 2.2.2
Tokenizers 0.12.1