Added whats needed

.gitignore

@@ -0,0 +1,4 @@
+*~
+model*
+vocab*
+venv/*

README.md

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+
+"He Said She Said" classification challenge (2nd edition)
+=========================================================
+
+Give the probability that a text in Polish was written by a man.
+
+This challenge is based on the "He Said She Said" corpus for Polish.
+The corpus was created by grepping gender-specific first person
+expressions (e.g. "zrobiłem/zrobiłam", "jestem zadowolony/zadowolona",
+"będę robił/robiła") in the Common Crawl corpus. Such expressions were
+normalised here into masculine forms.
+
+Classes
+-------
+
+* `0` — text written by a woman
+* `1` — text written by a man
+
+Directory structure
+-------------------
+
+* `README.md` — this file
+* `config.txt` — configuration file
+* `train/` — directory with training data
+* `train/train.tsv.gz` — train set (gzipped), the class is given in the first column,
+  a text fragment in the second one
+* `train/meta.tsv.gz` — metadata (do not use during training)
+* `dev-0/` — directory with dev (test) data
+* `dev-0/in.tsv` — input data for the dev set (text fragments)
+* `dev-0/expected.tsv` — expected (reference) data for the dev set
+* `dev-0/meta.tsv` — metadata (not used during testing)
+* `dev-1/` — directory with extra dev (test) data
+* `dev-1/in.tsv` — input data for the extra dev set (text fragments)
+* `dev-1/expected.tsv` — expected (reference) data for the extra dev set
+* `dev-1/meta.tsv` — metadata (not used during testing)
+* `test-A` — directory with test data
+* `test-A/in.tsv` — input data for the test set (text fragments)
+* `test-A/expected.tsv` — expected (reference) data for the test set (hidden)

config.txt

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+--metric Likelihood --metric Accuracy --metric {Likelihood:N<Likelihood>,Accuracy:N<Accuracy>}P<2>{f<in[2]:for-humans>N<+H>,f<in[3]:contaminated>N<+C>,f<in[3]:not-contaminated>N<-C>}  --precision 5

dev-0/out.tsv

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src/predict.py

@@ -44,10 +44,7 @@ def main():
             one_hot = create_one_hot(vocab, line)
             one_hot_tensor = torch.tensor(one_hot, dtype=torch.float64).T
             y_predicted = model(one_hot_tensor.float())
-            if y_predicted > 0.5:
-                out.write('1\n')
-            elif y_predicted <= 0.5:
-                out.write('0\n')
+            out.write(f'{y_predicted}\n')
             if counter % 100 == 0:
                 print(f"{counter}")
             counter +=1

src/train.py

@@ -5,6 +5,7 @@ import sys
 import pickle
 import torch
 from Network import Network
+import random
 
 def clear_tokens(text):
     text = text.rstrip('\n').lower()
@@ -15,33 +16,40 @@ def clear_tokens(text):
 
 def create_vocab(texts_and_scores):
     vocab = {}
+    celling = 100000
     for text in texts_and_scores.keys():
         tokens = clear_tokens(text)
         for token in tokens:
             vocab[token] = 0
+        if len(vocab.keys()) > celling:
+            print(f"Short vocab length : {len(vocab.keys())}")
+            return vocab
     return vocab
 
 def create_one_hot(vocab, text):
     one_hot = dict(vocab)
     tokens = clear_tokens(text)
     for token in tokens:
-        one_hot[token] += 1
+        try:
+            one_hot[token] += 1
+        except KeyError:
+            pass
     return [[i] for i in one_hot.values()]
 
 def main():
-    if len(sys.argv) == 4 or len(sys.argv) == 3:
+    if len(sys.argv) == 4 or len(sys.argv) == 3 or len(sys.argv) == 5:
         pass
     else:
         print('Not sufficient number of args')
         return
-
+    print("Reading data")
     texts_and_scores = {}
     with open(sys.argv[1], 'r') as file_in, open(sys.argv[2], 'r') as file_exp:
         for line_in, line_exp in zip(file_in, file_exp):
             texts_and_scores[line_in] = int(line_exp.rstrip('\n'))
     print(f"Data read")
 
-    if len(sys.argv) == 4:
+    if len(sys.argv) == 5:
         print(f"Loading vocab from {sys.argv[3]}")
         with open(sys.argv[3], 'rb') as f:
             vocab = pickle.load(f)
@@ -57,6 +65,11 @@ def main():
     input_size = len(vocab)
 
     model = Network(input_size)
+    rand_num = random.uniform(1,500)
+    if len(sys.argv) == 5:
+        model.load_state_dict(torch.load(sys.argv[4]))
+        print(f"Starting from checkpoint {sys.argv[4]}")
+
     lr = 0.1
     optimizer = torch.optim.SGD(model.parameters(), lr=lr)
     criterion = torch.nn.BCELoss()
@@ -64,8 +77,9 @@ def main():
     print("Starting training")
     model.train()
     counter = 0
+    random_set = sorted(texts_and_scores.items(), key=lambda x: random.random())
     try:
-        for text, score in texts_and_scores.items():
+        for text, score in random_set:
             one_hot = create_one_hot(vocab, text)
             one_hot_tensor = torch.tensor(one_hot, dtype=torch.float64).T
             y = torch.tensor([[score]])
@@ -78,10 +92,10 @@ def main():
             if counter % 50 == 0:
                 print(f"{counter} : {loss}")
             if counter % 100 == 0:
-                print(f"Saving checkpoint model-{counter}-{lr}.ckpt")
-                torch.save(model.state_dict(), f"model-{counter}-{lr}.ckpt")
+                print(f"Saving checkpoint model-{counter}-{lr}-{rand_num}.ckpt")
+                torch.save(model.state_dict(), f"model-{counter}-{lr}-{rand_num}.ckpt")
             counter += 1
     except KeyboardInterrupt:
-        torch.save(model.state_dict(), f"model-interrupted.ckpt")
-    torch.save(model.state_dict(), f"model-final.ckpt")
+        torch.save(model.state_dict(), f"model-interrupted-{lr}-{rand_num}.ckpt")
+    torch.save(model.state_dict(), f"model-final-{lr}-{rand_num}.ckpt")
 main()

test-A/out.tsv

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+0.7156
+0.0171
+0.9889
+0.0059
+0.3557
+0.0706
+0.6441
+0.1192
+0.9071
+0.0488