stock-news-distilbert

Finetuned distilBERT model for stock news classification

This distilbert model was fine-tuned on 50.000 stock news articles using the HuggingFace adapter from Kern AI refinery. The articles consisted of the headlines plus abstract of the article. For the finetuning, a single NVidia K80 was used for about four hours.

Join our Discord if you have questions about this model: https://discord.gg/MdZyqSxKbe

DistilBERT is a smaller, faster and lighter version of BERT. It was trained by distilling BERT base and has 40% less parameters than bert-base-uncased. It runs 60% faster while preserving over 95% of BERT’s performances as measured on the GLUE language understanding benchmark. DistilBERT does not have token-type embeddings, pooler and retains only half of the layers from Google’s BERT.

Features

• The model can handle various text classification tasks, especially when it comes to stock and finance news sentiment classification.
• The output of the model are the three classes "positive", "neutral" and "negative" plus the models respective confidence score of the class.
• The model was fine-tuned on a custom datasets that was curated by Kern AI and labeled in our tool refinery.
• The model is currently supported by the PyTorch framework and can be easily deployed on various platforms using the HuggingFace Pipeline API.

Usage

To use the model, you need to install the HuggingFace Transformers library:

pip install transformers

Then you can load the model and the tokenizer from the HuggingFace Hub:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("KernAI/stock-news-distilbert")
tokenizer = AutoTokenizer.from_pretrained("KernAI/stock-news-distilbert")

To classify a single sentence or a sentence pair, you can use the HuggingFace Pipeline API:

from transformers import pipeline

classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
result = classifier("This is a positive sentence.")
print(result)
# [{'label': 'POSITIVE', 'score': 0.9998656511306763}]