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--- |
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license: apache-2.0 |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text-generation |
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--- |
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# Oracle Language Model |
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## Model Description |
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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. |
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## Intended Uses & Limitations |
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The Oracle model is designed for a wide range of natural language processing tasks, including but not limited to: |
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- Text Classification |
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- Named Entity Recognition |
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- Question Answering |
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- Sentiment Analysis |
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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. |
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## Training and Evaluation Data |
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The Oracle model combines the following pre-trained models: |
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- BERT (bert-base-uncased) |
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- RoBERTa (roberta-base) |
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- DistilBERT (distilbert-base-uncased) |
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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. |
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## Training Procedure |
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The Oracle model is created by: |
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1. Loading the pre-trained BERT, RoBERTa, and DistilBERT models. |
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2. Passing input through each model separately. |
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3. Concatenating the outputs of all models. |
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4. Passing the concatenated output through a linear layer to produce the final output. |
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## Ethical Considerations |
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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. |
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## Citation |
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If you use this model in your research, please cite: |
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``` |
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@misc{oracle-language-model, |
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author = {Your Name}, |
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title = {Oracle: A Combined Language Model}, |
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year = {2024}, |
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publisher = {HuggingFace}, |
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journal = {HuggingFace Model Hub}, |
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howpublished = {\url{https://huggingface.co/your-username/oracle-model}} |
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} |
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``` |
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## Usage |
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Here's a simple example of how to use the Oracle model: |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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# Load model and tokenizer |
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model = AutoModel.from_pretrained("your-username/oracle-model") |
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tokenizer = AutoTokenizer.from_pretrained("your-username/oracle-model") |
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# Prepare input |
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text = "Hello, I am Oracle!" |
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inputs = tokenizer(text, return_tensors="pt") |
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# Forward pass |
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outputs = model(**inputs) |
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# Process outputs |
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embeddings = outputs.last_hidden_state |
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``` |
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For more detailed usage instructions and examples, please refer to the model card on the Hugging Face Model Hub. |