Oracle Language Model

Model Description

Oracle is a combined language model that leverages the strengths of multiple pre-trained models to create a more powerful and versatile model. It combines BERT, RoBERTa, and DistilBERT into a single model, allowing it to benefit from the unique characteristics of each.

Intended Uses & Limitations

The Oracle model is designed for a wide range of natural language processing tasks, including but not limited to:

Text Classification
Named Entity Recognition
Question Answering
Sentiment Analysis

As this is a combined model, it may have a larger computational footprint than individual models. Users should consider the trade-off between performance and computational resources.

Training and Evaluation Data

The Oracle model combines the following pre-trained models:

BERT (bert-base-uncased)
RoBERTa (roberta-base)
DistilBERT (distilbert-base-uncased)

Each of these models was trained on its respective datasets. The Oracle model itself does not undergo additional pre-training but rather combines the outputs of these pre-trained models.

Training Procedure

The Oracle model is created by:

Loading the pre-trained BERT, RoBERTa, and DistilBERT models.
Passing input through each model separately.
Concatenating the outputs of all models.
Passing the concatenated output through a linear layer to produce the final output.

Ethical Considerations

As the Oracle model combines multiple pre-trained models, it may amplify biases present in any of the individual models. Users should be aware of potential biases and evaluate the model's output carefully, especially for sensitive applications.

Citation

If you use this model in your research, please cite:

@misc{oracle-language-model,
  author = {Your Name},
  title = {Oracle: A Combined Language Model},
  year = {2024},
  publisher = {HuggingFace},
  journal = {HuggingFace Model Hub},
  howpublished = {\url{https://huggingface.co/your-username/oracle-model}}
}

Usage

Here's a simple example of how to use the Oracle model:

from transformers import AutoTokenizer, AutoModel

# Load model and tokenizer
model = AutoModel.from_pretrained("your-username/oracle-model")
tokenizer = AutoTokenizer.from_pretrained("your-username/oracle-model")

# Prepare input
text = "Hello, I am Oracle!"
inputs = tokenizer(text, return_tensors="pt")

# Forward pass
outputs = model(**inputs)

# Process outputs
embeddings = outputs.last_hidden_state

For more detailed usage instructions and examples, please refer to the model card on the Hugging Face Model Hub.