|
--- |
|
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 |
|
|
|
``` |
|
|
|
|
