--- license: apache-2.0 --- # YusufDagdeviren/SentimentAnalysisFromMovieReviews This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/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](https://www.kaggle.com/datasets/lakshmi25npathi/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 ======== Training... Batch 30 of 1,222. Elapsed: 0:00:38. Batch 60 of 1,222. Elapsed: 0:01:16. Batch 90 of 1,222. Elapsed: 0:01:53. Batch 120 of 1,222. Elapsed: 0:02:30. Batch 150 of 1,222. Elapsed: 0:03:07. Batch 180 of 1,222. Elapsed: 0:03:44. Batch 210 of 1,222. 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Elapsed: 0:20:26. Batch 1,020 of 1,222. Elapsed: 0:21:03. Batch 1,050 of 1,222. Elapsed: 0:21:40. Batch 1,080 of 1,222. Elapsed: 0:22:17. Batch 1,110 of 1,222. Elapsed: 0:22:54. Batch 1,140 of 1,222. Elapsed: 0:23:31. Batch 1,170 of 1,222. Elapsed: 0:24:09. Batch 1,200 of 1,222. Elapsed: 0:24:46. Average training loss: 0.27 Training epoch took: 0:25:12 Running Validation... Accuracy: 0.92 Validation took: 0:02:51 ======== Epoch 2 / 2 ======== Training... Batch 30 of 1,222. Elapsed: 0:00:37. Batch 60 of 1,222. Elapsed: 0:01:14. Batch 90 of 1,222. Elapsed: 0:01:51. Batch 120 of 1,222. Elapsed: 0:02:29. Batch 150 of 1,222. Elapsed: 0:03:06. Batch 180 of 1,222. Elapsed: 0:03:43. Batch 210 of 1,222. Elapsed: 0:04:20. Batch 240 of 1,222. Elapsed: 0:04:57. Batch 270 of 1,222. Elapsed: 0:05:34. Batch 300 of 1,222. Elapsed: 0:06:11. Batch 330 of 1,222. Elapsed: 0:06:48. Batch 360 of 1,222. Elapsed: 0:07:25. Batch 390 of 1,222. Elapsed: 0:08:03. Batch 420 of 1,222. Elapsed: 0:08:40. 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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 - Transformers 4.41.2 - Pytorch 2.3 - Tokenizers 0.19.1