Metadata

.gitignore

@@ -1 +1,2 @@
 data/
+__pycache__

README.md

@@ -0,0 +1,5 @@
+This is a multi-class, multi-label network that categorises text into one of ~160 categories, mostly relating to the African continent.
+
+The training dataset is a proprietry dataset from allAfrica.com, consisting of stories that have been manuially categorised according to AllAfrica's inhouse categorisation scheme.
+
+The trained model is freely available, as is the training and evaluation code.

africat/aa_create_dataset.py

@@ -191,3 +191,5 @@ def main():
 
 if __name__ == "__main__":
   main()
+
+# vim: set expandtab shiftwidth=2 softtabstop=2:

africat/categorise.py

@@ -14,8 +14,7 @@ import random
 import pandas as pd
 import numpy as np
 import itertools
-#from pandarallel import pandarallel
-from tqdm import tqdm
+import tqdm
 # torch
 import torch
 import torchdata.datapipes as dp
@@ -30,15 +29,15 @@ story_num = 64 # XXX None for all
 
 # Hyperparameters
 EPOCHS = 10       # epoch
-#LR = 5            # learning rate
-LR = 0.005 # initial learning rate; too small may result in a long training process that could get stuck, whereas a value too large may result in learning a sub-optimal set of weights too fast or an unstable training process -- perhaps the most important hyperparameter. If you have time to tune only one hyperparameter, tune the learning rate
+#LR = 5           # learning rate
+LR = 0.005        # initial learning rate; too small may result in a long training process that could get stuck, whereas a value too large may result in learning a sub-optimal set of weights too fast or an unstable training process -- perhaps the most important hyperparameter. If you have time to tune only one hyperparameter, tune the learning rate
 BATCH_SIZE = 64   # batch size for training
 
 def read_csv(input_csv, rows=None, verbose=0):
   if verbose > 0:
     with open(input_csv, 'r', encoding="utf-8") as f:
       data = pd.concat(
-        [chunk for chunk in tqdm(
+        [chunk for chunk in tqdm.tqdm(
           pd.read_csv(f,
             encoding="utf-8",
             quoting=csv.QUOTE_ALL,
@@ -345,7 +344,6 @@ def hot_one_to_labels(vocab, tensor):
 
 
 def train(dataloader, dataset, model, optimizer, criterion):
-  model.train()
   total_acc, total_count = 0, 0
   log_interval = 500
   start_time = time.time()
@@ -377,6 +375,7 @@ def train(dataloader, dataset, model, optimizer, criterion):
     [pprint.pprint(x) for x in hot_one_to_labels(dataset.cats_vocab, output)]
 
     return
+
     total_acc += (output == cats).sum().item()
     total_count += cats.size(0)
     if idx % log_interval == 0 and idx > 0:
@@ -556,8 +555,15 @@ def main():
 
   total_accu = None
   for epoch in range(1, EPOCHS + 1):
+    model.train()
     epoch_start_time = time.time()
-    train(train_dataloader, train_dataset,  model, optimizer, criterion)
+    with tqdm.trange(BATCH_SIZE, unit="batch", mininterval=0) as bar:
+        bar.set_description(f"Epoch {epoch}")
+        train(train_dataloader, train_dataset,  model, optimizer, criterion)
+        bar.set_postfix(
+        #    loss=float(loss),
+        #    acc=float(acc)
+        )
     accu_val = evaluate(valid_dataloader, valid_dataset, model, criterion)
     if total_accu is not None and total_accu > accu_val:
       scheduler.step()
@@ -581,3 +587,4 @@ def main():
 if __name__ == "__main__":
   main()
 
+# vim: set expandtab shiftwidth=2 softtabstop=2: