Updated READ.me

README.md

@@ -7,38 +7,25 @@ model-index:
   results: []
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-<!-- This model card has been generated automatically according to the information Keras had access to. You should
-probably proofread and complete it, then remove this comment. -->
-
-# tmpjy56pamo
-
-This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Train Loss: 0.0386
-- Train Accuracy: 0.9878
-- Validation Loss: 0.4790
-- Validation Accuracy: 0.8621
-- Epoch: 2
 
 ## Model description
 
-More information needed
+The rotten-tomatoes-model is a text-classification model. It used the `bert-base-cased` model, and was fine tuned on the `rotten_tomatoes` model. 
+
+After inputting a movie review, the model will output its prediction of how positive/negative the review is. LABEL_0 is Negative, while LABEL_1 is Positive.   
 
 ## Intended uses & limitations
 
-More information needed
+This model can be used to take in movie reviews and predict whether the overall sentiments of the review are positive or negative.
 
-## Training and evaluation data
+An example use case for this model is taking in reviews spanning from the start of the pandemic to the current time to see how sentiments surrounding movies might have been affected by when in the pandemic it was released (or other factors such as the method it was released
 
-More information needed
 
-## Training procedure
+## Training and evaluation data
 
-### Training hyperparameters
+As mentioned above, this 
 
-The following hyperparameters were used during training:
-- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 3198, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
-- training_precision: float32
+## Training procedure
 
 ### Training results