[[["เข้าใจง่าย","easyToUnderstand","thumb-up"],["แก้ปัญหาของฉันได้","solvedMyProblem","thumb-up"],["อื่นๆ","otherUp","thumb-up"]],[["ไม่มีข้อมูลที่ฉันต้องการ","missingTheInformationINeed","thumb-down"],["ซับซ้อนเกินไป/มีหลายขั้นตอนมากเกินไป","tooComplicatedTooManySteps","thumb-down"],["ล้าสมัย","outOfDate","thumb-down"],["ปัญหาเกี่ยวกับการแปล","translationIssue","thumb-down"],["ตัวอย่าง/ปัญหาเกี่ยวกับโค้ด","samplesCodeIssue","thumb-down"],["อื่นๆ","otherDown","thumb-down"]],["อัปเดตล่าสุด 2024-10-23 UTC"],[],[],null,["\u003cbr /\u003e\n\n[Agile classifiers](https://arxiv.org/pdf/2302.06541.pdf) is an efficient and flexible method\nfor creating custom content policy classifiers by tuning models, such as Gemma,\nto fit your needs. They also allow you complete control over where and how they\nare deployed.\n\n**Gemma Agile Classifier Tutorials**\n\n|---|---------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------|\n| | [Start Codelab](https://codelabs.developers.google.com/codelabs/responsible-ai/agile-classifiers) | [Start Google Colab](https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/agile_classifiers.ipynb) |\n\n\u003cbr /\u003e\n\nThe [codelab](https://codelabs.developers.google.com/codelabs/responsible-ai/agile-classifiers) and\n[tutorial](/gemma/docs/agile_classifiers) use [LoRA](https://arxiv.org/abs/2106.09685) to fine-tune a Gemma\nmodel to act as a content policy classifier using the [KerasNLP](https://keras.io/keras_nlp/)\nlibrary. Using only 200 examples from the [ETHOS dataset](https://paperswithcode.com/dataset/ethos), this\nclassifier achieves an [F1 score](https://en.wikipedia.org/wiki/F-score) of 0.80 and [ROC-AUC score](https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc#AUC)\nof 0.78, which compares favorably to state of the art\n[leaderboard results](https://paperswithcode.com/sota/hate-speech-detection-on-ethos-binary). When trained on the 800 examples,\nlike the other classifiers on the leaderboard, the Gemma-based agile classifier\nachieves an F1 score of 83.74 and a ROC-AUC score of 88.17. You can adapt the\ntutorial instructions to further refine this classifier, or to create your own\ncustom safety classifier safeguards."]]
เริ่ม Codelab
เริ่ม Google Colab