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

IMDB Dataset of 50K Movie Reviews

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

======== Epoch 1 / 2 ========
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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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Batch 960 of 1,222. Elapsed: 0:19:48.
Batch 990 of 1,222. Elapsed: 0:20:25.
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Batch 1,140 of 1,222. Elapsed: 0:23:31.
Batch 1,170 of 1,222. Elapsed: 0:24:08.
Batch 1,200 of 1,222. Elapsed: 0:24:45.

Average training loss: 0.16
Training epoch took: 0:25:12

Running Validation...
Accuracy: 0.93
Validation took: 0:02:52

Framework Versions