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--- |
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license: apache-2.0 |
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--- |
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# YusufDagdeviren/SentimentAnalysisFromMovieReviews |
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This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the imdb dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.16 |
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- Accuracy: 0.93 |
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- F1: 0.93 |
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## Model Description |
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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. |
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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. |
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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. |
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## Training and Evaluation Data |
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[IMDB Dataset of 50K Movie Reviews](https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews) |
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### Training Hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-5 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- total_train_batch_size: 38 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-6 |
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- lr_scheduler_type: linear |
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- num_epochs: 2 |
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### Training Results |
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======== Epoch 1 / 2 ======== |
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Training... |
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Batch 30 of 1,222. Elapsed: 0:00:38. |
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Batch 60 of 1,222. Elapsed: 0:01:16. |
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Batch 90 of 1,222. Elapsed: 0:01:53. |
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Batch 120 of 1,222. Elapsed: 0:02:30. |
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Batch 150 of 1,222. Elapsed: 0:03:07. |
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Batch 180 of 1,222. Elapsed: 0:03:44. |
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Batch 210 of 1,222. Elapsed: 0:04:21. |
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Batch 240 of 1,222. Elapsed: 0:04:58. |
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Batch 270 of 1,222. Elapsed: 0:05:35. |
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Batch 300 of 1,222. Elapsed: 0:06:12. |
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Batch 330 of 1,222. Elapsed: 0:06:49. |
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Batch 360 of 1,222. Elapsed: 0:07:27. |
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Batch 390 of 1,222. Elapsed: 0:08:04. |
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Batch 420 of 1,222. Elapsed: 0:08:41. |
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Batch 450 of 1,222. Elapsed: 0:09:18. |
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Batch 480 of 1,222. Elapsed: 0:09:55. |
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Batch 510 of 1,222. Elapsed: 0:10:32. |
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Batch 540 of 1,222. Elapsed: 0:11:09. |
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Batch 570 of 1,222. Elapsed: 0:11:46. |
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Batch 600 of 1,222. Elapsed: 0:12:24. |
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Batch 630 of 1,222. Elapsed: 0:13:01. |
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Batch 660 of 1,222. Elapsed: 0:13:38. |
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Batch 690 of 1,222. Elapsed: 0:14:15. |
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Batch 720 of 1,222. Elapsed: 0:14:52. |
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Batch 750 of 1,222. Elapsed: 0:15:29. |
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Batch 780 of 1,222. Elapsed: 0:16:06. |
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Batch 810 of 1,222. Elapsed: 0:16:43. |
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Batch 840 of 1,222. Elapsed: 0:17:20. |
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Batch 870 of 1,222. Elapsed: 0:17:57. |
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Batch 900 of 1,222. Elapsed: 0:18:35. |
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Batch 930 of 1,222. Elapsed: 0:19:12. |
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Batch 960 of 1,222. Elapsed: 0:19:49. |
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Batch 990 of 1,222. Elapsed: 0:20:26. |
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Batch 1,020 of 1,222. Elapsed: 0:21:03. |
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Batch 1,050 of 1,222. Elapsed: 0:21:40. |
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Batch 1,080 of 1,222. Elapsed: 0:22:17. |
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Batch 1,110 of 1,222. Elapsed: 0:22:54. |
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Batch 1,140 of 1,222. Elapsed: 0:23:31. |
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Batch 1,170 of 1,222. Elapsed: 0:24:09. |
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Batch 1,200 of 1,222. Elapsed: 0:24:46. |
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Average training loss: 0.27 |
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Training epoch took: 0:25:12 |
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Running Validation... |
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Accuracy: 0.92 |
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Validation took: 0:02:51 |
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======== Epoch 2 / 2 ======== |
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Training... |
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Batch 30 of 1,222. Elapsed: 0:00:37. |
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Batch 60 of 1,222. Elapsed: 0:01:14. |
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Batch 90 of 1,222. Elapsed: 0:01:51. |
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Batch 120 of 1,222. Elapsed: 0:02:29. |
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Batch 150 of 1,222. Elapsed: 0:03:06. |
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Batch 180 of 1,222. Elapsed: 0:03:43. |
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Batch 210 of 1,222. Elapsed: 0:04:20. |
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Batch 240 of 1,222. Elapsed: 0:04:57. |
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Batch 270 of 1,222. Elapsed: 0:05:34. |
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Batch 300 of 1,222. Elapsed: 0:06:11. |
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Batch 330 of 1,222. Elapsed: 0:06:48. |
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Batch 360 of 1,222. Elapsed: 0:07:25. |
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Batch 390 of 1,222. Elapsed: 0:08:03. |
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Batch 420 of 1,222. Elapsed: 0:08:40. |
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Batch 450 of 1,222. Elapsed: 0:09:17. |
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Batch 480 of 1,222. Elapsed: 0:09:54. |
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Batch 510 of 1,222. Elapsed: 0:10:31. |
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Batch 540 of 1,222. Elapsed: 0:11:08. |
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Batch 570 of 1,222. Elapsed: 0:11:45. |
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Batch 600 of 1,222. Elapsed: 0:12:23. |
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Batch 630 of 1,222. Elapsed: 0:13:00. |
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Batch 660 of 1,222. Elapsed: 0:13:37. |
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Batch 690 of 1,222. Elapsed: 0:14:14. |
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Batch 720 of 1,222. Elapsed: 0:14:51. |
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Batch 750 of 1,222. Elapsed: 0:15:28. |
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Batch 780 of 1,222. Elapsed: 0:16:05. |
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Batch 810 of 1,222. Elapsed: 0:16:43. |
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Batch 840 of 1,222. Elapsed: 0:17:20. |
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Batch 870 of 1,222. Elapsed: 0:17:57. |
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Batch 900 of 1,222. Elapsed: 0:18:34. |
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Batch 930 of 1,222. Elapsed: 0:19:11. |
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Batch 960 of 1,222. Elapsed: 0:19:48. |
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Batch 990 of 1,222. Elapsed: 0:20:25. |
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Batch 1,020 of 1,222. Elapsed: 0:21:03. |
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Batch 1,050 of 1,222. Elapsed: 0:21:40. |
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Batch 1,080 of 1,222. Elapsed: 0:22:17. |
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Batch 1,110 of 1,222. Elapsed: 0:22:54. |
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Batch 1,140 of 1,222. Elapsed: 0:23:31. |
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Batch 1,170 of 1,222. Elapsed: 0:24:08. |
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Batch 1,200 of 1,222. Elapsed: 0:24:45. |
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Average training loss: 0.16 |
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Training epoch took: 0:25:12 |
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Running Validation... |
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Accuracy: 0.93 |
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Validation took: 0:02:52 |
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### Framework Versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3 |
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- Tokenizers 0.19.1 |
