Update README.md

README.md

@@ -44,6 +44,20 @@ https://huggingface.co/finiteautomata/bertweet-base-sentiment-analysis
 
 https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest
 
+In order to use the HuggingFace space for our application, we had to create an empty model on Huggingface 
+initially. From there, we included the information box as shown on the top of this README. We created a
+secret key token on github that is linked with our HuggingFace account and used that key to create an 
+action file or .yml. This file ensures that everytime there is an update to main, the website on HuggingFace
+would start building based on the updated code.
+
+By using the streamlit library, we were able to incorporate the pipeline function which allows us to access
+and use a pretrained model from HuggingFace with ease. We created an app interface with includes a textbox
+and a selection menu which allows users to input any text into the textbox before selecting the model that
+they would like to use. The models used are listed above. Each of the models output a resulting sentiment
+analysis of the input text as well as a probability score, which we used with the help of the pipeline 
+functionality to output back onto HuggingFace's interface for the user to see. This was done for all three
+models.
+
 Milestone 3
 
 Finetuned Model URL: https://huggingface.co/dahongj/finetuned_toxictweets
@@ -57,4 +71,18 @@ into variables and ran through a Dataset class. A tokenizer for Distilbert was c
 Then using the multivariable version of the distilbert-base-uncased model because there are 6 forms 
 of toxicity included in the dataset that we want to finetune for. Using the native pytorch method
 of training as demonstrated on the HuggingFace documentation, the model was trained and evaluated.
-Both the finetuned model and its tokenizer are saved and uploaded onto HuggingFace.
+Both the finetuned model and its tokenizer are saved and uploaded onto HuggingFace.
+
+Milestone 4
+
+Results:
+The resulting web application on HuggingFace is a sentiment analysis application that allows users
+to input a text of any kind and receive results of the toxicity levels. The first three pretrained
+model has only two variances in the output, stating whether the text is majority positive or negative
+as well as the degree that it is so. The fourth option on the selection bar allows users to select
+our finetuned model, which determines six levels of toxicity: toxic, severe_toxic, obscene, insult,
+threat, and identity_hate. This option determines the initial toxicity level as well as the second
+highest level of toxicity. An example of 10 texts and their results are shown as an image on the website.
+
+The landing page for the application and a video to demonstrate how to use the application are included
+on this github.