its working

main.py

@@ -9,6 +9,9 @@ from collections import Counter, OrderedDict
 import spacy
 from torchcrf import CRF
 from torch.utils.data import DataLoader
+import numpy as np
+from sklearn.metrics import accuracy_score, f1_score, classification_report
+
 
 nlp = spacy.load('en_core_web_sm')
 
@@ -31,7 +34,7 @@ class Model(torch.nn.Module):
         out = self.hidden2tag(out)
         out = self.crf(out, tags.T)
         # out = self.sigm(self.fc1(torch.tensor([out])))
-        return out
+        return -out
 
     def decode(self, data):
         emb = self.relu(self.emb(data))
@@ -41,6 +44,12 @@ class Model(torch.nn.Module):
         out = self.crf.decode(out)
         return out
 
+    def train_mode(self):
+        self.crf.train()
+    
+    def eval_mode(self):
+        self.crf.eval()
+
 
 def process_document(document):
     # return [str(tok.lemma) for tok in nlp(document)]
@@ -62,7 +71,7 @@ def data_process(dt):
 
 def labels_process(dt):
     return [ torch.tensor([labels_vocab[token]  for token in  document.split(" ") ], dtype = torch.long) for document in dt]
-
+save_path = "train/out.tsv"
 data = pd.read_csv("train/train.tsv", sep="\t")
 data.columns = ["labels", "text"]
 
@@ -81,12 +90,14 @@ labels_vocab = {
     'I-ORG': 8
 }
 
+inv_labels_vocab = {v: k for k, v in labels_vocab.items()}
+
 train_tokens_ids = data_process(data["text"])
 train_labels = labels_process(data["labels"])
 
 num_tags = 9
 NUM_EPOCHS = 5
-seq_length = 15
+seq_length = 5
 
 model = Model(num_tags, seq_length)
 device = torch.device("cuda")
@@ -99,13 +110,14 @@ optimizer = torch.optim.Adam(model.parameters())
 train_dataloader = DataLoader(list(zip(train_tokens_ids, train_labels)), batch_size=64, shuffle=True)
 # test_dataloader = DataLoader(train_labels, batch_size=64, shuffle=True)
 
-mode = "train"
-# mode = "eval"
+# mode = "train"
+mode = "eval"
 # mode = "generate"
 
 if mode == "train":
     for i in range(NUM_EPOCHS):
         model.train()
+        model.train_mode()
         #for i in tqdm(range(500)):
         for i in tqdm(range(len(train_labels))):
             for k in range(0, len(train_tokens_ids[i]) - seq_length, seq_length):
@@ -114,22 +126,41 @@ if mode == "train":
                 
                 predicted_tags = model(batch_tokens.to(device), tags.to(device))
 
-                optimizer.zero_grad()
                 # tags = torch.tensor([x[0] for x in tags])
                 # loss  = criterion(predicted_tags.unsqueeze(0),tags.T)
                 
                 predicted_tags.backward()
                 optimizer.step()
                 model.zero_grad()
+                model.crf.zero_grad()
+                optimizer.zero_grad()
 
     torch.save(model.state_dict(), "model.torch")
 
 if mode == "eval":
     model.eval()
-    for i in tqdm(range(len(train_labels))):
+    model.eval_mode()
+    predicted = []
+    correct = []
+    model.load_state_dict(torch.load("model.torch"))
+    for i in tqdm(range(0, len(train_labels))):
         for k in range(0, len(train_tokens_ids[i]) - seq_length, seq_length):
             batch_tokens = train_tokens_ids[i][k: k + seq_length].unsqueeze(0)
             tags = train_labels[i][k: k + seq_length].unsqueeze(1)
-            
             predicted_tags = model.decode(batch_tokens.to(device))
-            print('dupa')
+            predicted += predicted_tags[0]
+            correct += [x[0] for x in tags.numpy().tolist()]
+    print(classification_report(correct, predicted))
+    print(accuracy_score(correct, predicted))
+    print(f1_score(correct, predicted, average="weighted"))
+    
+    predicted = list(map(lambda x: inv_labels_vocab[x], predicted))
+    slices = [len(x.split(" ")) for x in data["text"]]
+    with open(save_path, "a") as save:
+        accumulator = 0
+        for slice in slices:
+            save.write(' '.join(predicted[accumulator: accumulator + slice]))
+            accumulator += slice
+
+
+