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README.md
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@@ -127,7 +127,7 @@ The final evaluation cross-entropy ended around 0.4.
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Since the fine-tuned model is designed to explain, and if possible, summarize newly learned data, ROUGE and BERTScore metrics were measured on a sample of 50 manually crafted questions. The reference answers were constructed during the creation of the training and evaluation sets.
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Given that GPT-4-turbo was already used in this context for the reference questions generation, I did not compare my model against it. Instead, I chose to compare it against the following models:
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| Metric | quantum-research-bot-v1.0 | Meta-Llama-3.1-8B-Instruct | gemini-1.5-pro |
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|:------------------|:---------------------------|:--------------------|:------------------|
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| **BERTScore F1** | 0.5821 | 0.3305 | 0.4982 |
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| **ROUGE-1** | 0.6045 | 0.3152 |0.5029 |
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Since the fine-tuned model is designed to explain, and if possible, summarize newly learned data, ROUGE and BERTScore metrics were measured on a sample of 50 manually crafted questions. The reference answers were constructed during the creation of the training and evaluation sets.
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| 128 |
Given that GPT-4-turbo was already used in this context for the reference questions generation, I did not compare my model against it. Instead, I chose to compare it against the following models:
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| Metric (mean) | quantum-research-bot-v1.0 | Meta-Llama-3.1-8B-Instruct | gemini-1.5-pro |
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|:------------------|:---------------------------|:--------------------|:------------------|
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| **BERTScore F1** | 0.5821 | 0.3305 | 0.4982 |
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| 133 |
| **ROUGE-1** | 0.6045 | 0.3152 |0.5029 |
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