|
|
--- |
|
|
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. |
