transformers safetensors oracle text-generation en license:apache-2.0 endpoints_compatible region:us
---
language:
- en
library_name: transformers
license: apache-2.0
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.