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| title: AnalogyArcade | |
| emoji: 🏆 | |
| colorFrom: blue | |
| colorTo: yellow | |
| sdk: gradio | |
| sdk_version: 4.8.0 | |
| app_file: app.py | |
| pinned: false | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
| ## 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 | |
| [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. | |
| ### In-Context | |
| ## User Guide | |
| ### Introduction | |
| ### Usage | |
| ### Documentation | |
| ### Experiments | |
| ### Limitations | |