## bert-base-uncased finetuned on IMDB dataset Evaluation set was created by taking 1000 samples from test set ``` DatasetDict({ train: Dataset({ features: ['text', 'label'], num_rows: 25000 }) dev: Dataset({ features: ['text', 'label'], num_rows: 1000 }) test: Dataset({ features: ['text', 'label'], num_rows: 24000 }) }) ``` ## Parameters ``` max_sequence_length = 128 batch_size = 32 eval_steps = 100 learning_rate=2e-05 num_train_epochs=5 early_stopping_patience = 10 ``` ## Training Run ``` [2700/3910 1:11:43 < 32:09, 0.63 it/s, Epoch 3/5] Step Training Loss Validation Loss Accuracy Precision Recall F1 Runtime Samples Per Second 100 No log 0.371974 0.845000 0.798942 0.917004 0.853911 15.256900 65.544000 200 No log 0.349631 0.850000 0.873913 0.813765 0.842767 15.288600 65.408000 300 No log 0.359376 0.845000 0.869281 0.807692 0.837356 15.303900 65.343000 400 No log 0.307613 0.870000 0.851351 0.892713 0.871542 15.358400 65.111000 500 0.364500 0.309362 0.856000 0.807018 0.931174 0.864662 15.326100 65.248000 600 0.364500 0.302709 0.867000 0.881607 0.844130 0.862461 15.324400 65.255000 700 0.364500 0.300102 0.871000 0.894168 0.838057 0.865204 15.474900 64.621000 800 0.364500 0.383784 0.866000 0.833333 0.910931 0.870406 15.380100 65.019000 900 0.364500 0.309934 0.874000 0.881743 0.860324 0.870902 15.358900 65.109000 1000 0.254600 0.332236 0.872000 0.894397 0.840081 0.866388 15.442700 64.756000 1100 0.254600 0.330807 0.871000 0.877847 0.858300 0.867963 15.410900 64.889000 1200 0.254600 0.352724 0.872000 0.925581 0.805668 0.861472 15.272800 65.476000 1300 0.254600 0.278529 0.881000 0.891441 0.864372 0.877698 15.408200 64.900000 1400 0.254600 0.291371 0.878000 0.854962 0.906883 0.880157 15.427400 64.820000 1500 0.208400 0.324827 0.869000 0.904232 0.821862 0.861082 15.338600 65.195000 1600 0.208400 0.377024 0.884000 0.898734 0.862348 0.880165 15.414500 64.874000 1700 0.208400 0.375274 0.885000 0.881288 0.886640 0.883956 15.367200 65.073000 1800 0.208400 0.378904 0.880000 0.877016 0.880567 0.878788 15.363900 65.088000 1900 0.208400 0.410517 0.874000 0.866534 0.880567 0.873494 15.324700 65.254000 2000 0.130800 0.404030 0.876000 0.888655 0.856275 0.872165 15.414200 64.875000 2100 0.130800 0.390763 0.883000 0.882353 0.880567 0.881459 15.341500 65.183000 2200 0.130800 0.417967 0.880000 0.875502 0.882591 0.879032 15.351300 65.141000 2300 0.130800 0.390974 0.883000 0.898520 0.860324 0.879007 15.396100 64.952000 2400 0.130800 0.479739 0.874000 0.856589 0.894737 0.875248 15.460500 64.681000 2500 0.098400 0.473215 0.875000 0.883576 0.860324 0.871795 15.392200 64.968000 2600 0.098400 0.532294 0.872000 0.889362 0.846154 0.867220 15.364100 65.087000 2700 0.098400 0.536664 0.881000 0.880325 0.878543 0.879433 15.351100 65.142000 TrainOutput(global_step=2700, training_loss=0.2004435383832013, metrics={'train_runtime': 4304.5331, 'train_samples_per_second': 0.908, 'total_flos': 7258763970957312, 'epoch': 3.45}) ``` ## Classification Report ``` precision recall f1-score support 0 0.90 0.87 0.89 11994 1 0.87 0.90 0.89 12006 accuracy 0.89 24000 macro avg 0.89 0.89 0.89 24000 weighted avg 0.89 0.89 0.89 24000 ```