You can deploy this model and start earning money today!

abegweit/
my_awesome_wnut_JHn

Name: my_awesome_wnut_JHn
Availability: InStock

generated_from_trainer

distilbert/distilbert-base-uncased

Views

No views yet

my_awesome_wnut_JHn

This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0268
Precision: 0.8640
Recall: 0.9206
F1: 0.8914
Accuracy: 0.9897

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: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	46	0.1126	0.7312	0.5467	0.6257	0.9607
No log	2.0	92	0.0358	0.8643	0.8925	0.8782	0.9888
No log	3.0	138	0.0275	0.8634	0.9159	0.8889	0.9894
No log	4.0	184	0.0268	0.8640	0.9206	0.8914	0.9897

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cpu
Datasets 2.18.0
Tokenizers 0.15.2

abegweit/my_awesome_wnut_JHn

my_awesome_wnut_JHn

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

abegweit/
my_awesome_wnut_JHn