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Dmytro Struk
51af5eedc9
### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> This example demonstrates how to perform quality check on LLM results for such tasks as text summarization and translation with Semantic Kernel Filters. Metrics used in this example: - [BERTScore](https://github.com/Tiiiger/bert_score) - leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference sentences by cosine similarity. - [BLEU](https://en.wikipedia.org/wiki/BLEU) (BiLingual Evaluation Understudy) - evaluates the quality of text which has been machine-translated from one natural language to another. - [METEOR](https://en.wikipedia.org/wiki/METEOR) (Metric for Evaluation of Translation with Explicit ORdering) - evaluates the similarity between the generated summary and the reference summary, taking into account grammar and semantics. - [COMET](https://unbabel.github.io/COMET) (Crosslingual Optimized Metric for Evaluation of Translation) - is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments. In this example, SK Filters call dedicated server which is responsible for task evaluation using metrics described above. If evaluation score of specific metric doesn't meet configured threshold, an exception is thrown with evaluation details. [Hugging Face Evaluate Metric](https://github.com/huggingface/evaluate) library is used to evaluate summarization and translation results. ### Contribution Checklist <!-- Before submitting this PR, please make sure: --> - [x] The code builds clean without any errors or warnings - [x] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [x] All unit tests pass, and I have added new tests where possible - [x] I didn't break anyone 😄
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9 lines
70 B
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fastapi
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uvicorn
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pydantic
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bert_score
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nltk
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evaluate
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cmake
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unbabel-comet
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