test_wnut_model – AI Model by blambert | AlphaNeural AI

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blambert/
test_wnut_model

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token-classification

generated_from_trainer

distilbert/distilbert-base-uncased

autotrain_compatible

endpoints_compatible

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test_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: 0.2816
Precision: 0.5219
Recall: 0.3540
F1: 0.4219
Accuracy: 0.9427

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: 5e-06
train_batch_size: 6
eval_batch_size: 32
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
0.3664	1.0	566	0.3082	0.4777	0.1687	0.2493	0.9354
0.1672	2.0	1132	0.2867	0.5395	0.3105	0.3941	0.9407
0.1265	3.0	1698	0.3171	0.5976	0.2753	0.3769	0.9413
0.116	4.0	2264	0.2914	0.5712	0.3420	0.4278	0.9431
0.0974	5.0	2830	0.2816	0.5219	0.3540	0.4219	0.9427

Framework versions

Transformers 4.31.0
Pytorch 2.0.0
Datasets 2.14.2
Tokenizers 0.13.3