diff --git a/seq.py b/seq.py
index a7f447b..847ea74 100644
--- a/seq.py
+++ b/seq.py
@@ -7,25 +7,96 @@ import pandas as pd
 from sklearn.model_selection import train_test_split
 from collections import Counter
 from torchtext.vocab import Vocab
+from TorchCRF import CRF
+from tqdm import tqdm
 
+EPOCHS = 5
+BATCH = 1
 
 # Functions from jupyter
+
+
 def build_vocab(dataset):
     counter = Counter()
     for document in dataset:
         counter.update(document)
-    return Vocab(counter, specials=['<unk>', '<pad>', '<bos>', '<eos>'])
+    return Vocab(counter)
 
 
 def data_process(dt, vocab):
     return [torch.tensor([vocab['<bos>']] + [vocab[token] for token in document] + [vocab['<eos>']], dtype=torch.long) for document in dt]
 
 
-def labels_process(dt, vocab):
-    return [torch.tensor([0] + document + [0], dtype=torch.long) for document in dt]
+def get_scores(y_true, y_pred):
+    acc_score = 0
+    tp = 0
+    fp = 0
+    selected_items = 0
+    relevant_items = 0
 
+    for p, t in zip(y_pred, y_true):
+        if p == t:
+            acc_score += 1
+
+        if p > 0 and p == t:
+            tp += 1
+
+        if p > 0:
+            selected_items += 1
+
+        if t > 0:
+            relevant_items += 1
+
+    if selected_items == 0:
+        precision = 1.0
+    else:
+        precision = tp / selected_items
+
+    if relevant_items == 0:
+        recall = 1.0
+    else:
+        recall = tp / relevant_items
+
+    if precision + recall == 0.0:
+        f1 = 0.0
+    else:
+        f1 = 2 * precision * recall / (precision + recall)
+
+    return precision, recall, f1
+
+
+def eval_model(dataset_tokens, dataset_labels, model):
+    Y_true = []
+    Y_pred = []
+    for i in tqdm(range(len(dataset_labels))):
+        batch_tokens = dataset_tokens[i].unsqueeze(0)
+        tags = list(dataset_labels[i].numpy())
+        Y_true += tags
+
+        Y_batch_pred_weights = model(batch_tokens).squeeze(0)
+        Y_batch_pred = torch.argmax(Y_batch_pred_weights, 1)
+        Y_pred += list(Y_batch_pred.numpy())
+
+    return get_scores(Y_true, Y_pred)
+
+
+class LSTM(torch.nn.Module):
+    def __init__(self, vocab_len):
+        super(LSTM, self).__init__()
+        self.emb = torch.nn.Embedding(vocab_len, 100)
+        self.rec = torch.nn.LSTM(100, 256, 1, batch_first=True)
+        self.fc1 = torch.nn.Linear(256, 9)
+
+    def forward(self, x):
+        emb = torch.relu(self.emb(x))
+        lstm_output, (h_n, c_n) = self.rec(emb)
+        out_weights = self.fc1(lstm_output)
+
+        return out_weights
 
 # Load data
+
+
 def load_data():
     train = pd.read_csv('train/train.tsv', sep='\t',
                         names=['labels', 'document'])
@@ -44,5 +115,35 @@ def load_data():
     return X_train, Y_train, X_dev, Y_dev, X_test
 
 
+def train(model, crf, train_tokens, labels_tokens):
+    for i in range(EPOCHS):
+        crf.train()
+        model.train()
+        for i in tqdm(range(len(labels_tokens))):
+            batch_tokens = train_tokens[i].unsqueeze(0)
+            tags = labels_tokens[i].unsqueeze(1)
+
+            predicted_tags = model(batch_tokens).squeeze(0).unsqueeze(1)
+
+            optimizer.zero_grad()
+            loss = criterion(predicted_tags.squeeze(0), tags.squeeze(1))
+
+            loss.backward()
+            optimizer.step()
+
+
 if __name__ == "__main__":
     X_train, Y_train, X_dev, Y_dev, X_test = load_data()
+    vocab_x = build_vocab(X_train)
+    vocab_y = build_vocab(Y_train)
+    train_tokens = data_process(X_train, vocab_x)
+    labels_tokens = data_process(Y_train, vocab_y)
+    print(train_tokens[0])
+
+    # model
+    model = LSTM(len(vocab_x))
+    crf = CRF(9)
+    p = list(model.parameters()) + list(crf.parameters())
+    optimizer = torch.optim.Adam(p)
+    criterion = torch.nn.CrossEntropyLoss()
+    train(model, crf, train_tokens, labels_tokens)