diff --git a/generator/ctrl/ctrl_generator.py b/generator/ctrl/ctrl_generator.py
index ef50cef..31239c9 100644
--- a/generator/ctrl/ctrl_generator.py
+++ b/generator/ctrl/ctrl_generator.py
@@ -221,7 +221,7 @@ class CTRLGenerator():
                             "Edit", "&@@", "2:","1:", ":", "Edit@@", "EDI@@", "EDIT@@", "edit", "TL@@", "tl@@", ";@@",
                             '**', "http://@@", "Redd@@", "UP@@", "mom", "Up@@", "Me:", "Update", "mom@@", "Part",
                             "http://www.@@", "edit@@", "*@@", "Writing", "Text@@", "\\@@", "<br>@@", "<div", "|@@", '...',
-                            '..', 'https://@@]
+                            '..', 'https://@@']
 
         for forbidden_token in forbidden_tokens:
             prompt_logits[_token][self.word2idx[forbidden_token]] = -1e8
diff --git a/generator/pytorch/pytorch_generator.py b/generator/pytorch/pytorch_generator.py
deleted file mode 100644
index 3ee3d6d..0000000
--- a/generator/pytorch/pytorch_generator.py
+++ /dev/null
@@ -1,155 +0,0 @@
-# From https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_generation.py
-
-import argparse
-import logging
-from tqdm import trange
-
-import torch
-import torch.nn.functional as F
-import numpy as np
-
-from pytorch_transformers import GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig
-
-from pytorch_transformers import GPT2LMHeadModel, GPT2Tokenizer
-from pytorch_transformers import OpenAIGPTLMHeadModel, OpenAIGPTTokenizer
-from pytorch_transformers import XLNetLMHeadModel, XLNetTokenizer
-from pytorch_transformers import TransfoXLLMHeadModel, TransfoXLTokenizer
-
-
-logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
-                    datefmt = '%m/%d/%Y %H:%M:%S',
-                    level = logging.INFO)
-logger = logging.getLogger(__name__)
-
-MAX_LENGTH = int(10000)  # Hardcoded max length to avoid infinite loop
-
-ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig)), ())
-
-MODEL_CLASSES = {
-    'gpt2': (GPT2LMHeadModel, GPT2Tokenizer),
-    'openai-gpt': (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
-    'xlnet': (XLNetLMHeadModel, XLNetTokenizer),
-    'transfo-xl': (TransfoXLLMHeadModel, TransfoXLTokenizer),
-}
-
-# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
-# in https://github.com/rusiaaman/XLNet-gen#methodology
-# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
-PADDING_TEXT = """ In 1991, the remains of Russian Tsar Nicholas II and his family
-(except for Alexei and Maria) are discovered.
-The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
-remainder of the story. 1883 Western Siberia,
-a young Grigori Rasputin is asked by his father and a group of men to perform magic.
-Rasputin has a vision and denounces one of the men as a horse thief. Although his
-father initially slaps him for making such an accusation, Rasputin watches as the
-man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
-the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
-with people, even a bishop, begging for his blessing. <eod> </s> <eos>"""
-
-
-def set_seed(seed, n_gpu=1):
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    if n_gpu > 0:
-        torch.cuda.manual_seed_all(seed)
-
-
-def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')):
-    """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
-        Args:
-            logits: logits distribution shape (vocabulary size)
-            top_k > 0: keep only top k tokens with highest probability (top-k filtering).
-            top_p > 0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
-                Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
-        From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
-    """
-    assert logits.dim() == 1  # batch size 1 for now - could be updated for more but the code would be less clear
-    top_k = min(top_k, logits.size(-1))  # Safety check
-    if top_k > 0:
-        # Remove all tokens with a probability less than the last token of the top-k
-        indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
-        logits[indices_to_remove] = filter_value
-
-    if top_p > 0.0:
-        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
-        cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
-
-        # Remove tokens with cumulative probability above the threshold
-        sorted_indices_to_remove = cumulative_probs > top_p
-        # Shift the indices to the right to keep also the first token above the threshold
-        sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
-        sorted_indices_to_remove[..., 0] = 0
-
-        indices_to_remove = sorted_indices[sorted_indices_to_remove]
-        logits[indices_to_remove] = filter_value
-    return logits
-
-
-def sample_sequence(model, length, context, num_samples=1, temperature=1, top_k=0, top_p=0.0, is_xlnet=False, device='cpu'):
-    context = torch.tensor(context, dtype=torch.long, device=device)
-    context = context.unsqueeze(0).repeat(num_samples, 1)
-    generated = context
-    with torch.no_grad():
-        for _ in trange(length):
-
-            inputs = {'input_ids': generated}
-            if is_xlnet:
-                # XLNet is a direct (predict same token, not next token) and bi-directional model by default
-                # => need one additional dummy token in the input (will be masked), attention mask and target mapping (see model docstring)
-                input_ids = torch.cat((generated, torch.zeros((1, 1), dtype=torch.long, device=device)), dim=1)
-                perm_mask = torch.zeros((1, input_ids.shape[1], input_ids.shape[1]), dtype=torch.float, device=device)
-                perm_mask[:, :, -1] = 1.0  # Previous tokens don't see last token
-                target_mapping = torch.zeros((1, 1, input_ids.shape[1]), dtype=torch.float, device=device)
-                target_mapping[0, 0, -1] = 1.0  # predict last token
-                inputs = {'input_ids': input_ids, 'perm_mask': perm_mask, 'target_mapping': target_mapping}
-
-            outputs = model(**inputs)  # Note: we could also use 'past' with GPT-2/Transfo-XL/XLNet (cached hidden-states)
-            next_token_logits = outputs[0][0, -1, :] / temperature
-            filtered_logits = top_k_top_p_filtering(next_token_logits, top_k=top_k, top_p=top_p)
-            next_token = torch.multinomial(F.softmax(filtered_logits, dim=-1), num_samples=1)
-            generated = torch.cat((generated, next_token.unsqueeze(0)), dim=1)
-    return generated
-
-
-def main():
-
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    seed = 150
-    set_seed(seed)
-
-    model_type = "gpt2"
-    model_name = "gpt2-medium"
-    model_class, tokenizer_class = MODEL_CLASSES[model_type]
-    tokenizer = tokenizer_class.from_pretrained(model_name)
-    model = model_class.from_pretrained(model_name)
-    model.to(device)
-    model.eval()
-
-    temperature = 0.9
-    top_k = 40
-    top_p = 1.0
-
-    length = 100
-    while True:
-        raw_text = input("Model prompt >>> ")
-        if model_type in ["transfo-xl", "xlnet"]:
-            # Models with memory likes to have a long prompt for short inputs.
-            raw_text = (PADDING_TEXT) + raw_text
-        context_tokens = tokenizer.encode(raw_text)
-        out = sample_sequence(
-            model=model,
-            context=context_tokens,
-            length=length,
-            temperature=temperature,
-            top_k=top_k,
-            top_p=top_p,
-            device=device,
-            is_xlnet=bool(model_type == "xlnet")
-        )
-        out = out[0, len(context_tokens):].tolist()
-        text = tokenizer.decode(out, clean_up_tokenization_spaces=True)
-        print(text)
-
-
-if __name__ == '__main__':
-    main()
\ No newline at end of file
diff --git a/data/storytree.py b/other/storytree.py
similarity index 100%
rename from data/storytree.py
rename to other/storytree.py