Create README.md

README.md

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+# 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}]
+```