amazon_review_classification

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: 1.3976
• Accuracy: 0.6732

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	Accuracy
No log	1.0	115	1.0703	0.6732
No log	2.0	230	1.2393	0.6341
No log	3.0	345	1.1084	0.6683
No log	4.0	460	1.1262	0.6829
0.3201	5.0	575	1.3179	0.6732
0.3201	6.0	690	1.3832	0.6585
0.3201	7.0	805	1.2997	0.6683
0.3201	8.0	920	1.3872	0.6634
0.0863	9.0	1035	1.3832	0.6634
0.0863	10.0	1150	1.3976	0.6732

Framework versions

• Transformers 4.38.2
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2

Usage

from transformers import pipeline

classifier = pipeline("sentiment-analysis", model="eren23/amazon_review_classification")
classifier(text)