ner-classifier-distil-bert – AI Model by florenciopaucar | AlphaNeural AI

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florenciopaucar/
ner-classifier-distil-bert

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token-classification

generated_from_trainer

distilbert/distilbert-base-cased

autotrain_compatible

endpoints_compatible

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ner-classifier-distil-bert

This model is a fine-tuned version of distilbert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0484
Precision: 0.9237
Recall: 0.9388
F1: 0.9312
Accuracy: 0.9929

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0319	1.0	3546	0.0326	0.8726	0.9023	0.8872	0.9882
0.0206	2.0	7092	0.0285	0.8967	0.9273	0.9117	0.9910
0.0133	3.0	10638	0.0295	0.9072	0.9307	0.9188	0.9916
0.0081	4.0	14184	0.0327	0.9127	0.9274	0.9200	0.9918
0.0038	5.0	17730	0.0346	0.9205	0.9297	0.9251	0.9922
0.0044	6.0	21276	0.0376	0.9258	0.9299	0.9278	0.9925
0.0017	7.0	24822	0.0427	0.9277	0.9256	0.9266	0.9923
0.0038	8.0	28368	0.0460	0.9170	0.9399	0.9283	0.9926
0.0008	9.0	31914	0.0473	0.9266	0.9344	0.9305	0.9928
0.0026	10.0	35460	0.0484	0.9237	0.9388	0.9312	0.9929

Framework versions

Transformers 4.38.1
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.2