Update README.md

README.md

@@ -11,17 +11,45 @@ pinned: false
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
-## Information
-### Database
-[ag_news]([https://huggingface.co/datasets/glue](https://huggingface.co/datasets/ag_news)
+## Model Types
+### Baseline
+For my dataset, I made use of  relbert/analogy_questions on huggingface, which has all data in the format of:
+```
+"stem": ["raphael", "painter"],
+"answer": 2,
+"choice": [["andersen", "plato"],
+          ["reading", "berkshire"],
+          ["marx", "philosopher"],
+          ["tolstoi", "edison"]]
+```
+
+For a baseline, if I were to do a random selection for answer to train the system on (so the stem analogy is compared to a random choice among the answers), then there would only be a 25% baseline for correct categorization and comparison.
+
+### Bag-of-Words Model
+For comparison, I made use of my previously trained bag-of-words model from [our previous project](https://github.com/smhavens/NLPHW03).
+
+### Fine-Tuning
+#### Dataset
+[analogy questions dataset](https://huggingface.co/datasets/relbert/analogy_questions)
 
 This database uses a text with label format, with each label being an integer between 0 and 3, relating to the 4 main categories of the news:  World (0), Sports (1), Business (2), Sci/Tech (3).
 
 I chose this one because of the larger variety of categories compared to sentiment databases, with the themes/categories theoretically being more closely related to analogies. I also chose ag_news because, as a news source, it should avoid slang and other potential hiccups that databases using tweets or general reviews will have.
 
-### Pre-trained model
+#### Pre-trained model
 [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
 
 Because my focus is on using embeddings to evaluate analogies for the AnalogyArcade, I focused my model search for those in the sentence-transformers category, as they are readily made for embedding usage. I chose all-MiniLM-L6-v2 because of its high usage and good reviews: it is a well trained model but smaller and more efficient than its previous version.
 
-TESTING README UPDATE
+### In-Context
+
+## User Guide
+### Introduction
+
+### Usage
+
+### Documentation
+
+### Experiments
+
+### Limitations