my_awesome_wnut_model – AI Model by sarahh23 | AlphaNeural AI

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sarahh23/
my_awesome_wnut_model

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token-classification

generated_from_trainer

distilbert/distilbert-base-uncased

autotrain_compatible

endpoints_compatible

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my_awesome_wnut_model

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: 0.2885
Precision: 0.5508
Recall: 0.3818
F1: 0.4510
Accuracy: 0.9464

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	213	0.2879	0.5754	0.2122	0.3101	0.9378
No log	2.0	426	0.2657	0.5796	0.3207	0.4129	0.9435
0.1879	3.0	639	0.2710	0.5795	0.3411	0.4294	0.9452
0.1879	4.0	852	0.2823	0.5811	0.3587	0.4436	0.9461
0.0543	5.0	1065	0.2885	0.5508	0.3818	0.4510	0.9464

Framework versions

Transformers 4.42.3
Pytorch 2.1.2
Datasets 2.20.0
Tokenizers 0.19.1