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---
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
```
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