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Access to an all-inclusive stack for AI development, driven by NVIDIA’s powerful technology!
Enhance application capacity and availability.
Backup and restore data instantly, securely and maintain data integrity.
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(Standard) Test Criteria
(Standard) Test Criteria
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Click Add & update button
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The Tasks window appears. Select the task type:
- Text similarity: Measures similarity metrics between model outputs and reference texts.
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Click Next to open the list of available metrics
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Select one or more metrics
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Click Update to apply changes
The following metrics of Text similarity are available:
| Test criteria / Metric | How it tests | Best for |
|---|---|---|
| BLEU | Measures how precisely a model’s output matches reference text using n-gram overlap. | Evaluating translation and short text similarity. |
| Fuzzy Match | Measures how closely the model’s output resembles the reference text, allowing for minor differences in wording or order. | Checking approximate correctness. |
| ROUGE-1 | Measures unigram (single word) overlap between model output and reference text. | Short summarization and text generation tasks. |
| ROUGE-2 | Measures bigram (two-word sequence) overlap between model output and reference text. | Evaluating contextual accuracy. |
| ROUGE-L | Measures the longest common subsequence (LCS) between model output and reference text to capture fluency and word order similarity. | Longer text evaluation where structure matters. |
| ROUGE-LSUM | Measures LCS-based similarity across multiple sentences, suitable for evaluating longer summaries. | Summarization tasks. |
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