Update README.md

README.md

@@ -1,3 +1,142 @@
----
-license: apache-2.0
----
+---
+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.    Elapsed: 0:04:21.
+  Batch   240  of  1,222.    Elapsed: 0:04:58.
+  Batch   270  of  1,222.    Elapsed: 0:05:35.
+  Batch   300  of  1,222.    Elapsed: 0:06:12.
+  Batch   330  of  1,222.    Elapsed: 0:06:49.
+  Batch   360  of  1,222.    Elapsed: 0:07:27.
+  Batch   390  of  1,222.    Elapsed: 0:08:04.
+  Batch   420  of  1,222.    Elapsed: 0:08:41.
+  Batch   450  of  1,222.    Elapsed: 0:09:18.
+  Batch   480  of  1,222.    Elapsed: 0:09:55.
+  Batch   510  of  1,222.    Elapsed: 0:10:32.
+  Batch   540  of  1,222.    Elapsed: 0:11:09.
+  Batch   570  of  1,222.    Elapsed: 0:11:46.
+  Batch   600  of  1,222.    Elapsed: 0:12:24.
+  Batch   630  of  1,222.    Elapsed: 0:13:01.
+  Batch   660  of  1,222.    Elapsed: 0:13:38.
+  Batch   690  of  1,222.    Elapsed: 0:14:15.
+  Batch   720  of  1,222.    Elapsed: 0:14:52.
+  Batch   750  of  1,222.    Elapsed: 0:15:29.
+  Batch   780  of  1,222.    Elapsed: 0:16:06.
+  Batch   810  of  1,222.    Elapsed: 0:16:43.
+  Batch   840  of  1,222.    Elapsed: 0:17:20.
+  Batch   870  of  1,222.    Elapsed: 0:17:57.
+  Batch   900  of  1,222.    Elapsed: 0:18:35.
+  Batch   930  of  1,222.    Elapsed: 0:19:12.
+  Batch   960  of  1,222.    Elapsed: 0:19:49.
+  Batch   990  of  1,222.    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.
+  Batch   450  of  1,222.    Elapsed: 0:09:17.
+  Batch   480  of  1,222.    Elapsed: 0:09:54.
+  Batch   510  of  1,222.    Elapsed: 0:10:31.
+  Batch   540  of  1,222.    Elapsed: 0:11:08.
+  Batch   570  of  1,222.    Elapsed: 0:11:45.
+  Batch   600  of  1,222.    Elapsed: 0:12:23.
+  Batch   630  of  1,222.    Elapsed: 0:13:00.
+  Batch   660  of  1,222.    Elapsed: 0:13:37.
+  Batch   690  of  1,222.    Elapsed: 0:14:14.
+  Batch   720  of  1,222.    Elapsed: 0:14:51.
+  Batch   750  of  1,222.    Elapsed: 0:15:28.
+  Batch   780  of  1,222.    Elapsed: 0:16:05.
+  Batch   810  of  1,222.    Elapsed: 0:16:43.
+  Batch   840  of  1,222.    Elapsed: 0:17:20.
+  Batch   870  of  1,222.    Elapsed: 0:17:57.
+  Batch   900  of  1,222.    Elapsed: 0:18:34.
+  Batch   930  of  1,222.    Elapsed: 0:19:11.
+  Batch   960  of  1,222.    Elapsed: 0:19:48.
+  Batch   990  of  1,222.    Elapsed: 0:20:25.
+  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: 0:24:08.
+  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
+- Transformers 4.41.2
+- Pytorch 2.3
+- Tokenizers 0.19.1