bert-finetuned-ner-a1-a2-a3-a4-tok-1k-10k-e6-v1 – AI Model by divya1111 | AlphaNeural AI

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divya1111/
bert-finetuned-ner-a1-a2-a3-a4-tok-1k-10k-e6-v1

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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bert-finetuned-ner-a1-a2-a3-a4-tok-1k-10k-e6-v1

This model is a fine-tuned version of dslim/bert-large-NER on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0073
Precision: 0.8372
Recall: 0.9506
F1: 0.8903
Accuracy: 0.9976

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: 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: 6

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.008	1.0	1125	0.0076	0.8349	0.9253	0.8778	0.9973
0.0052	2.0	2250	0.0069	0.8300	0.9292	0.8768	0.9973
0.0038	3.0	3375	0.0071	0.8267	0.9397	0.8796	0.9973
0.0033	4.0	4500	0.0068	0.8322	0.9498	0.8871	0.9976
0.0023	5.0	5625	0.0066	0.8362	0.9478	0.8885	0.9976
0.002	6.0	6750	0.0073	0.8372	0.9506	0.8903	0.9976

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu117
Datasets 2.13.1
Tokenizers 0.13.3