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# System prepended metadata

title: Meeting with Fede 11/10

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# Meeting with Fede 11/10

1. Finished creating sequence classification models with 2 approaches - the first worked better, but still not all that great (precision drops a lot between the dev and test runs!).  For both, I made sure that the model's character truncation doesn't affect one source's descriptions far more than the others source's descriptions. 

    **Approach 1:** Taking an even number of matched and mismatched descriptions so there's a balance between the two classes (training size = 752)

    | data               | accuracy | precision | recall  | F1      |
    | ------------------ | -------- | --------- | ------- | ------- |
    | test               | 0.72930  | 0.00663   | 0.67424 | 0.01314 |
    | dev (first epoch)  | 0.64220  | 0.58861   | 0.85321 | 0.69663 |
    | dev (second epoch) | 0.64220  | 0.70130   | 0.49541 | 0.58065 |
    | dev (last epoch)   | 0.69266  | 0.675     | 0.74312 | 0.70742 |

    **Approach 2:** Randomly picking 20 Lewis descriptions and taking all their matched description pairs and a subset (also randomly selected) of the mismatched description pairs (training size = 2,024)

    | data               | accuracy | precision | recall  | F1      |
    | ------------------ | -------- | --------- | ------- | ------- |
    | test               | 0.99680  | 0.0       | 0.0     | 0.0     |
    | dev (first epoch)  | 0.98814  | 0.0       | 0.0     | 0.0     |
    | dev (second epoch) | 0.98715  | 0.0       | 0.0     | 0.0     |
    | dev (last epoch)   | 0.98715  | 0.0       | 0.0     | 0.0     |

2. Updated the main README for the repo and created a README for the classification directory

3. Trying to optimize models - different truncation approach, hyperparameter search, selecting best model over a number of epochs... are there other ways I should consider optimizing the models?

4. Next week, could work on extracting 19th century Wikipedia text to see if using those as features (perhaps in combination with the introductory descriptions?) help to improve the classification results.

5. Since I'll need to wrap up my work in the first week of December, in 2 weeks I'm also planning to try cloning the repo and run everything on a different computer to make sure everything will work smoothly for another person.

6. In London on November 22nd to meet with Ruth, Daniel and Katie at 3 PM to update them on results of the classification work, ask about any update from BHO on sharing derived Lewis data (will then update lewisprocessing/README accordingly).  Could also come in on Thursday morning or sometime Friday that week if you'd like to meet in person. 



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q: Abbas

1. fuzzy string matching

candidates:
- Abas (*) + wiki
- Abbats
- Abbast


1. embed the description and the wiki para
2. you measure cs similarity
3. you check if it's the best

abbas + wiki 

rq: if you train a classifier, would it be better than cosine similarity? 

