distilbert-base-uncased-imdb

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text_classification_model

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

• Loss: 0.2291
• Accuracy: 0.9318

Model description

More information needed

Intended uses & limitations

this model is intended for binary sentiment classification on English-languange text especially in domains such as:

Movie Reviews (e.g IMDB),

Product or Service feedback,

Social media Posts and short user comments,

Customer Satisfaction surveys.

While the model performs well on movie review-style sentiment classification, it has the following limitations:

Language restriction: Trained only on English; not suitable for other languages

Domain Generalization: May perfom poorly on domains very different from movie reviews(e.g. medical, legal, or financial text)

Subtle Sentiment: May misclassify sarcasm, irony, or ambigous statements

Biases: inherits potential biases from IMDB dataset, including genre-related language patterns and reviewer tendencies

Input Length: Long texts are truncated to fit the model's maximun sequence length, which may impact classification accuracy on lengthy documents

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 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

Framework versions

• Transformers 4.54.0
• Pytorch 2.6.0+cu124
• Datasets 4.0.0
• Tokenizers 0.21.2