Part of the idea for 11777 Group 3: Crisis Event Detection and Multimodal Image Classification. For the full project, please visit this repository.
First setup the environment with
pip install requirements.txt
Then, you can run training with commands in train.sh
to train the model. Make sure to adjust the paths to fit your own setup.
To generate categorical embeddings, run categorize.sh
.
svm score | average absolute correlation | max absolute correlation | uninformed guess | |
---|---|---|---|---|
seg parse task1 (raw, not selection) | 0.75065097385689 | 0.039936896992649754 | 0.2671258520103224 | 0.6608686595146339 |
seg parse task2 (raw, not selection) | 0.6856023506366308 | 0.03339309080332272 | 0.3145600649171777 | 0.5308521057786484 |
seg parse task1 (selected) | 0.7400270805124466 | 0.1631294098247213 | 0.2671258520103224 | 0.6608686595146339 |
seg parse task2 (selected) | 0.6622592229840026 | 0.18025121919956205 | 0.3145600649171777 | 0.5308521057786484 |
is raw task1 | (0.6629517758566816) | 0.17093582817624034 | 0.17093582817624034 | 0.6608686595146339 |
is raw task2 | (0.5308521057786484) | 0.046634066630442964 | 0.09947684241862445 | 0.5308521057786484 |
is text task1 | (0.6608686595146339) | 0.08242194982785796 | 0.08242194982785796 | 0.6608686595146339 |
is text task2 | (0.5308521057786484) | 0.06754924277721482 | 0.1054613934799101 | 0.5308521057786484 |
Used all training samples to evaluate extractors and select features