readme: ---
  license: afl-3.0
  ---
  
  # Overview
  
  This model is based on [bert-base-uncased](https://huggingface.co/bert-base-uncased) model and trained on more than 30k tweets that scraped from Twitter. By inputing some sentences with a '[MASK]' indicating the location you would like to fill in with a hashtag, our model can generate potential related trending topics according to your tweet context. 
  
  # Define a list of trending topics
  ```python
  trending_topics = [Your choice of topics]
  ```
  
  # Download the model
  ```python
  from transformers import pipeline, BertTokenizer 
  import numpy as np
  
  MODEL = "vivianhuang88/bert_twitter_hashtag"
  fill_mask = pipeline("fill-mask", model=MODEL, tokenizer=MODEL)
  tokenizer = BertTokenizer.from_pretrained(MODEL, additional_special_tokens=trending_topics)
  ```
  
  # Get the output
  ```python
  def print_candidates(text, candidates):
      for i in range(5):
          token = tokenizer.decode(candidates[i]['token'])
          topic = ''.join(token.split())
          output = text.replace("[MASK]", topic)
          print(output)
          
  text = "Bruce has an electric guitar set in [MASK]. "
  candidates = fill_mask(text, targets = trending_topics)
  print_candidates(text, candidates)
  
  ```