Up tókànNigbamii ti Ilé pẹlu AI Systems
Awọn apoti isura infomesonu Vector
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Èdè AI Itọsọna
Didara gbigba wiwọn boya eto wiwa pada si ẹri ti o wulo fun ibeere kan ati gbe e nibiti oluka tabi awoṣe isalẹ le lo.
Gbọ si itọsọna yiiAI-ṣe afihan · ~2 min
Ibaramu, agbegbe, alabapade, ati igbanilaaye gbogbo ṣe pataki. Dimegilio ibajọra giga nikan ko fihan pe ọrọ kan dahun ibeere naa.
Define relevance with the intended task in mind. A document about a product may be topically related but fail to answer a specific question about a version or date. Label examples of fully supporting evidence, partial evidence, and irrelevant material . Measure the candidate set and ranking lọtọ. Remember at a chosen cutoff asks how much relevant material was retrieved; precision asks how much of the retrieved material is relevant. Rank-aware metrics assess whether the best evidence appears early. State the cutoff and labeling method with every score. Inspect failure patterns: exact identifiers missed by semantic search, synonyms missed by keyword search, outdated documents ranked above current ones, or passages cut away from their qualifications. Hybrid retrieval and reranking can help some cases, but must be tested on the same fixed examples. Include access restrictions and unanswerable queries in the test set. A system should not improve apparent relevance by returning illegal documents. Nigbati ko si ẹri ti o peye, wiwọn boya ohun elo naa sọ idiwọn yẹn dipo ki o ṣe idahun ti ko ni atilẹyin.
04Apẹẹrẹ ṣiṣẹ
Ninu ikojọpọ ti a kọ, awọn ẹsẹ mẹrin dahun ibeere kan. Wiwa pada awọn ẹsẹ marun, eyiti mẹta jẹ pataki.
Konge ni marun jẹ 3/5 = 60%; ranti ni marun jẹ 3/4 = 75%.
Ṣayẹwo awọn sonu ti o yẹ aye ati awọn meji irrelevant esi ṣaaju ki o to yan a tuning ayipada.
Ohun ti o fihan
Awọn iṣiro wọnyi ṣe afihan awọn ohun-ini igbasilẹ oriṣiriṣi meji; ko si ẹnikan ti o ṣe iwọn idahun idahun ikẹhin.
Idanwo gbigba ti koodu aṣẹ gangan ati ibeere atilẹyin paraphrased.
Ṣayẹwo boya awọn ẹya eto imulo lọwọlọwọ kọja awọn ti a fipamọ.
Ìwé ìròyìn ọfẹ
Ìtàn AI mẹta tó jẹ́rìí ní gbogbo owurọ́ ọjọ́ iṣẹ́, kọ́wé ní Gẹ̀ẹ́sì tó rọrùn. Ọfẹ́ fún gbogbo àkókò, kò sí ìpolówó.
Ìmeèlì kan ní gbogbo ọjọ́ iṣẹ́. Yọkúrò ní ìkànìyàn ní ìkànìyàn kan. A kò ta tàbí pín àdírẹ́sì rẹ.
Ṣe idanwo fun ara rẹ
Ìdáhùn lẹ́sẹ̀kẹsẹ̀ lórí gbogbo ìdáhùn, àti iwe-ẹ̀rí tó lè pin pẹ̀lú ID tó lè jẹ́rìí lẹ́yìn tí o bá kọ́ ẹ̀kọ́.
Ṣe àtìlẹ́yìn ẹ̀kọ́ AI ọfẹ́. AI Understanding jẹ́ àjọ àìní èrè 501(c)(3) — kò sí ìpolówó, kò sí àkúnya owó, ní gbogbo àkókò. Ṣe ẹbun kan
Rárá. Awọn ọrọ diẹ sii le mu agbegbe dara si ṣugbọn tun ṣafikun ipo ti ko ṣe pataki tabi ariyanjiyan. Ṣe iwọn iṣowo ni ohun elo pipe.
Tesiwaju kikọ
Awọn itọsọna diẹ sii ti a yan fun koko yii
Up tókànNigbamii ti Ilé pẹlu AI Systems
Awọn apoti isura infomesonu Vector
Imọ-ẹrọ