---

library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags:

• generated_from_trainer metrics:
• accuracy
• precision
• recall
• f1 model-index:
• name: wk3ex_bert_imdb_sentiment results: [] datasets: Kaggle imdb dataseg

wk3ex_bert_imdb_sentiment

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.2804
• Accuracy: 0.9201
• Precision: 0.9201
• Recall: 0.9201
• F1: 0.9201

Model description

Exercise for University course. Finetuning for sentiment analysis with imdb Kaggle dataset

Intended uses & limitations

Sentiment analysis

Training and evaluation data

finetuning with imdb dataset

Training procedure

2 epochs

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.2405	1.0	2500	0.2392	0.9093	0.9107	0.9093	0.9092
0.1183	2.0	5000	0.2804	0.9201	0.9201	0.9201	0.9201

Framework versions

• Transformers 4.47.1
• Pytorch 2.5.1+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0