YusufDagdeviren/SentimentAnalysisFromMovieReviews
This model is a fine-tuned version of
xlnet-base-cased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.16
- Accuracy: 0.93
- F1: 0.93
Model Description
This project uses a fine-tuned XLNet model for sentiment analysis on English movie reviews. The model was fine-tuned using PyTorch and Huggingface Transformers libraries to improve its performance on sentiment classification tasks.
XLNet (eXtreme Language Model) is an autoregressive pre-training method that combines the best of BERT and Transformer-XL architectures, providing significant improvements in performance over traditional language models. This fine-tuned XLNet model aims to provide high accuracy and reliability in sentiment analysis.
The training process involved the use of the AdamW optimizer with a learning rate of 2e-5, betas of [0.9, 0.999], and epsilon of 1e-6. The model was trained for 2 epochs with a linear learning rate scheduler and no warmup steps.
Training and Evaluation Data
Training Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-5
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- total_train_batch_size: 38
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-6
- lr_scheduler_type: linear
- num_epochs: 2
Training Results
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Average training loss: 0.27
Training epoch took: 0:25:12
Running Validation...
Accuracy: 0.92
Validation took: 0:02:51
======== Epoch 2 / 2 ========
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Average training loss: 0.16
Training epoch took: 0:25:12
Running Validation...
Accuracy: 0.93
Validation took: 0:02:52
Framework Versions
- Transformers 4.41.2
- Pytorch 2.3
- Tokenizers 0.19.1
