| # 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}] | |
| ``` |