bert-wnut17-optimized – AI Model by mircoboettcher | AlphaNeural AI

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mircoboettcher/
bert-wnut17-optimized

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token-classification

generated_from_trainer

dslim/bert-base-NER

autotrain_compatible

endpoints_compatible

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bert-wnut17-optimized

This model is a fine-tuned version of dslim/bert-base-NER on the wnut_17 dataset. It achieves the following results on the evaluation set:

Loss: 0.2901
Precision: 0.5795
Recall: 0.3818
F1: 0.4603
Accuracy: 0.9485

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: 2.631245451057452e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	213	0.2365	0.5265	0.4235	0.4694	0.9478
No log	2.0	426	0.2692	0.5710	0.3689	0.4482	0.9480
0.2086	3.0	639	0.2901	0.5795	0.3818	0.4603	0.9485

Framework versions

Transformers 4.47.1
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0