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--- |
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library_name: transformers |
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tags: [] |
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--- |
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## Merged Model Performance |
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This repository contains the results of our merged rag relevance PEFT adapter model. |
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### RAG Relevance Classification Metrics |
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Our merged model achieves the following performance on a binary classification task: |
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``` |
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precision recall f1-score support |
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0 0.74 0.77 0.75 100 |
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1 0.76 0.73 0.74 100 |
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accuracy 0.75 200 |
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macro avg 0.75 0.75 0.75 200 |
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weighted avg 0.75 0.75 0.75 200 |
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``` |
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### Model Usage |
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For best results, we recommend starting with the following prompting strategy (and encourage tweaks as you see fit): |
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```python |
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def format_input_classification(query, text): |
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input = f""" |
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You are comparing a reference text to a question and trying to determine if the reference text |
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contains information relevant to answering the question. Here is the data: |
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[BEGIN DATA] |
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************ |
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[Question]: {query} |
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************ |
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[Reference text]: {text} |
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************ |
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[END DATA] |
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Compare the Question above to the Reference text. You must determine whether the Reference text |
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contains information that can answer the Question. Please focus on whether the very specific |
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question can be answered by the information in the Reference text. |
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Your response must be single word, either "relevant" or "unrelated", |
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and should not contain any text or characters aside from that word. |
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"unrelated" means that the reference text does not contain an answer to the Question. |
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"relevant" means the reference text contains an answer to the Question.""" |
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return input |
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text = format_input_classification("What is quanitzation?", |
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"Quantization is a method to reduce the memory footprint") |
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messages = [ |
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{"role": "user", "content": text} |
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] |
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pipe = pipeline( |
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"text-generation", |
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model=base_model, |
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model_kwargs={"attn_implementation": attn_implementation, "torch_dtype": torch.float16}, |
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tokenizer=tokenizer, |
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) |
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``` |
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### Comparison with Other Models |
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We compared our merged model's performance on the RAG Eval benchmark against several other state-of-the-art language models: |
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| Model | Precision | Recall | F1 | |
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|---------------------- |----------:|-------:|-------:| |
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| Our Merged Model | 0.74 | 0.77 | 0.75 | |
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| GPT-4 | 0.70 | 0.88 | 0.78 | |
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| GPT-4 Turbo | 0.68 | 0.91 | 0.78 | |
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| Gemini Pro | 0.61 | 1.00 | 0.76 | |
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| GPT-3.5 | 0.42 | 1.00 | 0.59 | |
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| Palm (Text Bison) | 0.53 | 1.00 | 0.69 | |
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[1] Scores from arize/phoenix |
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As shown in the table, our merged model achieves a comparable score of 0.75, outperforming several other black box models. |
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We will continue to improve and fine-tune our merged model to achieve even better performance across various benchmarks and tasks. |
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Citations: |
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[1] https://docs.arize.com/phoenix/evaluation/how-to-evals/running-pre-tested-evals/retrieval-rag-relevance |
