readme: ---
  language: en
  widget:
  - text: "I am going to buy 100 shares of cake tomorrow"
  ---
  
  # roberta-ticker: model was fine-tuned from Roberta to detect financial tickers
  
  ## Introduction
  
  This is a model specifically designed to identify tickers in text.
  Model was trained on transformed dataset from following Kaggle dataset:
  https://www.kaggle.com/omermetinn/tweets-about-the-top-companies-from-2015-to-2020
  
  
  
  ## How to use roberta-ticker with HuggingFace
  
  ##### Load roberta-ticker and its sub-word tokenizer :
  
  ```python
  from transformers import AutoTokenizer, AutoModelForTokenClassification
  
  tokenizer = AutoTokenizer.from_pretrained("Jean-Baptiste/roberta-ticker")
  model = AutoModelForTokenClassification.from_pretrained("Jean-Baptiste/roberta-ticker")
  
  
  ##### Process text sample 
  
  from transformers import pipeline
  
  nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
  
  nlp("I am going to buy 100 shares of cake tomorrow")
  [{'entity_group': 'TICKER',
    'score': 0.9612462520599365,
    'word': ' cake',
    'start': 32,
    'end': 36}]
    
  nlp("I am going to eat a cake tomorrow")
  []
   
  
  
  ```
  
  
  ## Model performances
  
  
  ```
  precision: 0.914157
  recall: 0.788824
  f1: 0.846878
  
   ```
  
  