Hemg/distilbert-base-uncased-finetuned-ner

Source: 🤗token-classificationapache-2.066M params267 MBsafetensors✓ 19 checksumsupdated 909d ago

distilbert-base-uncased-finetuned-ner

This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:

Loss: 0.0619
Precision: 0.9253
Recall: 0.9355
F1: 0.9304
Accuracy: 0.9834

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: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	439	0.0877	0.8779	0.8955	0.8866	0.9754
0.2182	2.0	878	0.0626	0.9193	0.9299	0.9245	0.9820
0.0557	3.0	1317	0.0612	0.9252	0.9323	0.9287	0.9829
0.0346	4.0	1756	0.0619	0.9253	0.9355	0.9304	0.9834

Framework versions

Transformers 4.39.3
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.2

Weights

Original

F32265 MB
2 files
model.safetensors 265 MBtraining_args.bin 5 kB

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