phobert-base-v2-finetuned-ner-thesis-dseb – AI Model by quangcodecode | AlphaNeural AI

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quangcodecode/
phobert-base-v2-finetuned-ner-thesis-dseb

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token-classification

generated_from_trainer

vinai/phobert-base-v2

autotrain_compatible

endpoints_compatible

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phobert-base-v2-finetuned-ner-thesis-dseb

This model is a fine-tuned version of vinai/phobert-base-v2 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.3842
Precision: 0.75
Recall: 0.8329
F1: 0.7893
Accuracy: 0.9491

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: 1e-05
train_batch_size: 32
eval_batch_size: 32
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
2.0671	1.0	23	1.4715	0.0	0.0	0.0	0.6125
1.3776	2.0	46	0.9706	0.9471	0.3860	0.5485	0.7545
0.9833	3.0	69	0.6568	0.8144	0.7207	0.7647	0.9034
0.7423	4.0	92	0.4905	0.9215	0.9045	0.9130	0.9595
0.5928	5.0	115	0.3919	0.9626	0.9517	0.9572	0.9893
0.4955	6.0	138	0.3377	0.9658	0.9579	0.9619	0.9913
0.4013	7.0	161	0.3058	0.9658	0.9579	0.9619	0.9915
0.3747	8.0	184	0.2874	0.9658	0.9579	0.9619	0.9915
0.3618	9.0	207	0.2781	0.9658	0.9579	0.9619	0.9915
0.3477	10.0	230	0.2748	0.9658	0.9579	0.9619	0.9915

Framework versions

Transformers 4.36.0
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0