my_awesome_wnut_model_3 – AI Model by Bania2001 | AlphaNeural AI | AlphaNeural AI

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Bania2001/
my_awesome_wnut_model_3

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token-classification

generated_from_trainer

almanach/camembert-base

autotrain_compatible

endpoints_compatible

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my_awesome_wnut_model_3

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

Loss: 0.0117
Precision: 0.0
Recall: 0.0
F1: 0.0
Accuracy: 0.9982

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	160	0.0408	0.0	0.0	0.0	0.9977
No log	2.0	320	0.0264	0.0	0.0	0.0	0.9981
No log	3.0	480	0.0213	0.0	0.0	0.0	0.9978
0.0751	4.0	640	0.0170	0.0	0.0	0.0	0.9982
0.0751	5.0	800	0.0152	0.0	0.0	0.0	0.9982
0.0751	6.0	960	0.0137	0.0	0.0	0.0	0.9981
0.0165	7.0	1120	0.0126	0.0	0.0	0.0	0.9982
0.0165	8.0	1280	0.0121	0.0	0.0	0.0	0.9980
0.0165	9.0	1440	0.0118	0.0	0.0	0.0	0.9982
0.0112	10.0	1600	0.0117	0.0	0.0	0.0	0.9982

Framework versions

Transformers 4.45.2
Pytorch 2.5.0
Datasets 2.19.0
Tokenizers 0.20.1