wnut_distilbert_NER_baseline – AI Model by perfectsquare123 | AlphaNeural AI

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perfectsquare123/
wnut_distilbert_NER_baseline

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token-classification

generated_from_trainer

distilbert/distilbert-base-uncased

autotrain_compatible

endpoints_compatible

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wnut_distilbert_NER_baseline

This model is a fine-tuned version of distilbert-base-uncased on the wnut_17 dataset. It achieves the following results on the evaluation set:

Loss: 0.2728
Precision: 0.5326
Recall: 0.3791
F1: 0.4429
Accuracy: 0.9453

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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.3978	1.0	107	0.3026	0.5514	0.1640	0.2529	0.9347
0.1425	2.0	214	0.2761	0.5853	0.2734	0.3727	0.9398
0.0979	3.0	321	0.2680	0.5428	0.3290	0.4097	0.9433
0.0744	4.0	428	0.2787	0.5339	0.3429	0.4176	0.9442
0.0651	5.0	535	0.2728	0.5326	0.3791	0.4429	0.9453

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0