YANX0014NKT
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NLP-DistilBERT-EE6483project

Text Classification

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text-embeddings-inference

Model card Files Files and versions

NLP-DistilBERT-EE6483project

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: 0.3015
Accuracy: 0.9487
F1: 0.9468

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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10

Training results

Framework versions

Transformers 4.46.1
Pytorch 2.4.1+cpu
Datasets 3.1.0
Tokenizers 0.20.1

Downloads last month: 6

Safetensors

Model size

67M params

Tensor type

F32

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distilbert/distilbert-base-uncased

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