copilot_wnut_model – AI Model by bobbyw | AlphaNeural AI

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bobbyw/
copilot_wnut_model

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token-classification

generated_from_trainer

distilbert/distilbert-base-uncased

autotrain_compatible

endpoints_compatible

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copilot_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: 0.3448
Precision: 0.5804
Recall: 0.4115
F1: 0.4816
Accuracy: 0.9484

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	213	0.2743	0.6364	0.2725	0.3816	0.9401
No log	2.0	426	0.2598	0.5977	0.3346	0.4290	0.9445
0.1759	3.0	639	0.3063	0.6741	0.3086	0.4234	0.9445
0.1759	4.0	852	0.3097	0.5930	0.3605	0.4484	0.9463
0.0477	5.0	1065	0.2962	0.5558	0.4106	0.4723	0.9474
0.0477	6.0	1278	0.3218	0.5792	0.3967	0.4708	0.9474
0.0477	7.0	1491	0.3199	0.5595	0.4096	0.4730	0.9477
0.022	8.0	1704	0.3385	0.5938	0.4106	0.4855	0.9481
0.022	9.0	1917	0.3311	0.5687	0.4217	0.4843	0.9478
0.0123	10.0	2130	0.3448	0.5804	0.4115	0.4816	0.9484

Framework versions

Transformers 4.34.1
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1