ayaat
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fine-tuned-DistilBert

Text Classification

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text-embeddings-inference

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fine-tuned-DistilBert

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: 0.9685
Accuracy: 80.3145
Precision: 84.1310
Recall: 80.3145
F1: 79.7509
Auc: 90.9373
Confusion Matrix: [[3664 2095] [ 171 5581]]

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: 3e-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

Framework versions

Transformers 4.41.2
Pytorch 2.3.1+cu121
Datasets 2.19.2
Tokenizers 0.19.1

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Safetensors

Model size

67M params

Tensor type

F32

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Base model

distilbert/distilbert-base-uncased

Finetuned

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