distilbert-base-uncased-finetuned-qqp – AI Model by mprov | AlphaNeural AI

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mprov/
distilbert-base-uncased-finetuned-qqp

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text-classification

generated_from_trainer

distilbert/distilbert-base-uncased

autotrain_compatible

endpoints_compatible

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distilbert-base-uncased-finetuned-qqp

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

Loss: 0.0080
Accuracy: 0.9991
F1: 0.9983

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-05
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	1.0	19	0.3840	0.75	0.0
No log	2.0	38	0.1577	0.9407	0.8882
No log	3.0	57	0.0891	0.9588	0.9216
No log	4.0	76	0.0495	0.9888	0.9774
No log	5.0	95	0.0156	0.9974	0.9948
No log	6.0	114	0.0107	0.9983	0.9966
No log	7.0	133	0.0080	0.9991	0.9983
No log	8.0	152	0.0074	0.9991	0.9983

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2