Create README.md

README.md

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+## 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
+```