# Finetuned destilBERT model for stock news classification

This is a HuggingFace model that uses BERT (Bidirectional Encoder Representations from Transformers) to perform text classification tasks. It was fine-tuned on 50.000 stock news articles using the HuggingFace adapter from Kern AI refinery.
BERT is a state-of-the-art pre-trained language model that can encode both the left and right context of a word in a sentence, allowing it to capture complex semantic and syntactic information.

## Features

- The model can handle various text classification tasks, especially when it comes to stock and finance news sentiment classification.
- The model can accept either single sentences or sentence pairs as input, and output a probability distribution over the predefined classes.
- The model can be fine-tuned on custom datasets and labels using the HuggingFace Trainer API or the PyTorch Lightning integration.
- The model is currently supported by the PyTorch framework and can be easily deployed on various platforms using the HuggingFace Pipeline API or the ONNX Runtime.

## Usage

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

```bash
pip install transformers
```
Then you can load the model and the tokenizer from the HuggingFace Hub:

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("KernAI/stock-news-destilbert")
tokenizer = AutoTokenizer.from_pretrained("KernAI/stock-news-destilbert")
```
To classify a single sentence or a sentence pair, you can use the HuggingFace Pipeline API:

```python
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}]
```