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---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
---
# 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:
1. Loading the pre-trained BERT, RoBERTa, and DistilBERT models.
2. Passing input through each model separately.
3. Concatenating the outputs of all models.
4. 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:
```python
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. |