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

Loss: 0.7385
Precision: 0.8305
Recall: 0.8507
F1: 0.8405
Accuracy: 0.9634

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: 4.73519627984468e-05
train_batch_size: 8
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.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: 0.0919159187150491
num_epochs: 10
label_smoothing_factor: 0.1089540797247441

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.7376	1.0	4250	0.7348	0.7949	0.8159	0.8053	0.9569
0.7181	2.0	8500	0.7202	0.8213	0.8292	0.8252	0.9608
0.7069	3.0	12750	0.7200	0.8015	0.8415	0.8210	0.9611
0.69	4.0	17000	0.7189	0.8223	0.8418	0.8320	0.9623
0.6789	5.0	21250	0.7225	0.8261	0.8500	0.8379	0.9635
0.6685	6.0	25500	0.7253	0.8298	0.8481	0.8389	0.9636
0.662	7.0	29750	0.7318	0.8275	0.8472	0.8372	0.9632
0.6542	8.0	34000	0.7335	0.8254	0.8504	0.8377	0.9632
0.6475	9.0	38250	0.7369	0.8297	0.8514	0.8404	0.9638
0.6464	10.0	42500	0.7385	0.8305	0.8507	0.8405	0.9634

Framework versions

Transformers 4.50.1
Pytorch 2.5.1+cu124
Datasets 3.4.1
Tokenizers 0.21.1

shellypeng/distilbert-base-cased-finetuned-ner-final

distilbert-base-cased-finetuned-ner-final

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

shellypeng/
distilbert-base-cased-finetuned-ner-final