Update README.md

README.md

@@ -28,31 +28,18 @@ This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co
 6. We then trained the same model on the noisy data and apply it to an held-out test set from the original set split.
 7. Training with couple of thousands noisy "positives" and "negatives" yielded a test set accuracy of about 95%.
 
-Accuracy results for Logistic Regression (LR) and BERT (base-cased) are shown below
-
-Accuracy |  Kaggle    |  Enhanced noisy data set
-LR       |   79.0%    |       95.1%
-BERT     |   88.7%    |       95.2%
-
-Here we describe the process in more detail alongside with other metrics -  https://drive.google.com/file/d/1MI9gRdppactVZ_XvhCwvoaOV1aRfprrd/view?usp=sharing 
-
-
+Accuracy results for Logistic Regression (LR) and BERT (base-cased) are shown in the attached pdf:
 
+https://drive.google.com/file/d/1MI9gRdppactVZ_XvhCwvoaOV1aRfprrd/view?usp=sharing 
 
 
 ## Model description
 
-More information needed
+BERT model trained on noisy data from search results. See PDF for more details.
 
 ## Intended uses & limitations
 
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
+Intended for use on finance news sentiment analysis with 3 options: "Positive", "Neutral" and "Negative"
 
 ### Training hyperparameters