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README.md

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----
-license: apache-2.0
----
+---
+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.