added README

README.md

@@ -9,36 +9,37 @@ metrics:
 model-index:
 - name: sentiment-analysis-browser-extension
   results: []
+language:
+- en
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# sentiment-analysis-browser-extension
+# Fine-tuned BERT model
+We open source this fine-tuned BERT model to identify critical aspects within user reviews of adblocking extensions. For every user review, the model provides a criticality score (in the range of -1 to 1) with the negative scores signifying higher probability of finding critical topics within in the reviews.
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. 
-It achieves the following results on the evaluation set:
-- Loss: 0.4768
-- Accuracy: 0.8615
-- F1: 0.8816
+We have used the [`distilbert-base-uncased`](https://huggingface.co/distilbert-base-uncased) as the base model and fine-tuned it on a manually annotated dataset of webstore reviews.
 
-## Model description
+Further details can be found in our AsiaCCS paper - [From User Insights to Actionable Metrics: A User-Focused Evaluation of Privacy-Preserving Browser Extensions](https://doi.org/10.1145/3634737.3657028).
 
-More information needed
+### Note
+We haven't tested its accuracy on user reviews from other categories but are open to discuss the possibility of extrapolating it to other product categories.
 
 ## Intended uses & limitations
+The model has been released to be freely used. It has not been trained on any private user data. Please do cite the above paper in our works. 
 
-More information needed
-
-## Training and evaluation data
+## Evaluation data
 
-More information needed
+It achieves the following results on the evaluation set:
+- Loss: 0.4768
+- Accuracy: 0.8615
+- F1: 0.8816
 
 ## Training procedure
 
-### Training hyperparameters
+The training dataset comprised on 620 reviews and the test dataset had 150 reviews. The following hyperparameters were used during training:
 
-The following hyperparameters were used during training:
 - learning_rate: 2e-05
 - train_batch_size: 16
 - eval_batch_size: 16
@@ -47,10 +48,6 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: linear
 - num_epochs: 6
 
-### Training results
-
-
-
 ### Framework versions
 
 - Transformers 4.34.0