AnalysisSentimentsReviewFilms

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

• Accuracy: 0.79
• Loss: 0.4532

Model description

This model is designed to analyze movie reviews and automatically classify the sentiment expressed in the text. It determines whether the review is positive or negative and assigns a confidence score indicating the probability of the prediction. The model processes the review's text content, identifies linguistic patterns, and evaluates the overall tone to generate its classification.

Intended uses & limitations

Intended Uses:

• Automatic classification of film reviews as positive or negative.

• Sentiment analysis on film review platforms.

• Support for recommendation systems based on user reviews.

• Monitoring audience perception of films.

Limitations:

• The model only distinguishes between positive and negative sentiment; it does not identify more complex nuances such as neutrality or irony.

• It may be less accurate with ambiguous, sarcastic, or figurative language.

Training and evaluation data

The model was trained using a dataset of film reviews manually labeled as positive or negative. The data includes texts of varying lengths and writing styles to improve the model's generalizability.

The dataset was divided into training, validation, and testing categories to evaluate performance and avoid overfitting. The metrics used for evaluation include accuracy, accuracy by class, recall, and F1 score.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

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

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	250	0.4532	0.79

Framework versions

• Transformers 5.2.0
• Pytorch 2.10.0+cpu
• Datasets 4.6.1
• Tokenizers 0.22.2