How to use from the
Use from the
sentence-transformers library
from sentence_transformers import CrossEncoder

model = CrossEncoder("KhoaUIT/Halong-CrossEncoder-UIT-R2GQA")

query = "Which planet is known as the Red Planet?"
passages = [
	"Venus is often called Earth's twin because of its similar size and proximity.",
	"Mars, known for its reddish appearance, is often referred to as the Red Planet.",
	"Jupiter, the largest planet in our solar system, has a prominent red spot.",
	"Saturn, famous for its rings, is sometimes mistaken for the Red Planet."
]

scores = model.predict([(query, passage) for passage in passages])
print(scores)

CrossEncoder based on KhoaUIT/Halong-UIT-R2GQA

This is a Cross Encoder model finetuned from KhoaUIT/Halong-UIT-R2GQA using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.

Model Details

Model Description

  • Model Type: Cross Encoder
  • Base model: KhoaUIT/Halong-UIT-R2GQA
  • Maximum Sequence Length: 512 tokens
  • Number of Output Labels: 1 label
  • Training Dataset: R2GQA
  • Language: vi

Model Sources

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import CrossEncoder

# Download from the ๐Ÿค— Hub
model = CrossEncoder("KhoaUIT/KhoaUIT-CrossEncoder-UIT-R2GQA")
# Get scores for pairs of texts
pairs = [
    ['Trฦฐแปng hแปฃp mแป™t SV ฤ‘ฦฐแปฃc CBCT cho xem bร i cแปงa SV khรกc trong giแป thi ฤ‘ฦฐแปฃc giแบฃi quyแบฟt nhฦฐ thแบฟ nร o?', 'ฤiแปu 22. Xแปญ lรฝ cรกn bแป™ vi phแบกm quy ฤ‘แป‹nh Trong thแปi gian thi, nแบฟu cรกc cรกn bแป™ thanh tra thi phรกt hiแป‡n thแบฅy cรกc trฦฐแปng hแปฃp vi phแบกm thรฌ lแบญp biรชn bแบฃn tแบกi chแป— vร  ฤ‘แป nghแป‹ Hiแป‡u trฦฐแปŸng xแปญ lรฝ. Ngฦฐแปi tham gia cรดng tรกc thi cรณ hร nh vi vi phแบกm quy ฤ‘แป‹nh (bแป‹ phรกt hiแป‡n trong khi lร m nhiแป‡m vแปฅ hoแบทc sau kแปณ thi), nแบฟu cรณ ฤ‘แปง chแปฉng cแปฉ, tรนy theo mแปฉc ฤ‘แป™, sแบฝ bแป‹ xแปญ lรฝ theo cรกc hรฌnh thแปฉc sau ฤ‘รขy. 1. Khiแปƒn trรกch ฤ‘แป‘i vแป›i nhแปฏng ngฦฐแปi phแบกm lแป—i nhแบน trong khi thi hร nh nhiแป‡m vแปฅ. 2. Cแบฃnh cรกo ฤ‘แป‘i vแป›i nhแปฏng ngฦฐแปi vi phแบกm mแป™t trong cรกc lแป—i sau ฤ‘รขy: a. CBCT bแป coi thi mร  khรดng thรดng bรกo cho ฤ‘ฦกn vแป‹ tแป• chแปฉc mรดn thi tแปซ 2 lแบงn trแปŸ lรชn trong mแป™t ฤ‘แปฃt thi. b. CBCT ฤ‘แปƒ cho SV tแปฑ do trao ฤ‘แป•i khi lร m bร i, sแปญ dแปฅng tร i liแป‡u bแป‹ cแบฅm sแปญ dแปฅng hoแบทc sแปญ dแปฅng cรกc phฦฐฦกng tiแป‡n thรดng tin liรชn lแบกc tแบกi phรฒng thi. c. Cรกn bแป™ chแบฅm thi chแบฅm ฤ‘iแปƒm hoแบทc cแป™ng ฤ‘iแปƒm bร i thi cรณ nhiแปu sai sรณt (trรชn 5% tแป•ng sแป‘ bร i thi). d. Cรกn bแป™ chแบฅm thi nแป™p ฤ‘iแปƒm trแป… so vแป›i thแปi hแบกn quy ฤ‘แป‹nh. 3. Tรนy theo mแปฉc ฤ‘แป™ vi phแบกm cรณ thแปƒ bแป‹ hแบก bแบญc lฦฐฦกng, hแบก ngแบกch, cรกch chแปฉc hoแบทc chuyแปƒn ฤ‘i lร m cรดng tรกc khรกc ฤ‘แป‘i vแป›i nhแปฏng ngฦฐแปi vi phแบกm mแป™t trong cรกc lแป—i sau ฤ‘รขy: a. Trแปฑc tiแบฟp giแบฃi bร i rแป“i hฦฐแป›ng dแบซn cho SV lรบc ฤ‘ang thi. b. Lแบฅy bร i thi cแปงa SV lร m ฤ‘ฦฐแปฃc giao cho SV khรกc. 4. Buแป™c thรดi viแป‡c hoแบทc bแป‹ xแปญ lรฝ theo phรกp luแบญt ฤ‘แป‘i vแป›i ngฦฐแปi cรณ mแป™t trong cรกc hร nh vi sai phแบกm sau ฤ‘รขy: a. Lร m lแป™ ฤ‘แป thi, mua, bรกn ฤ‘แป thi. b. ฤฦฐa ฤ‘แป thi ra ngoร i khu vแปฑc thi hoแบทc ฤ‘ฦฐa bร i giแบฃi tแปซ ngoร i vร o phรฒng thi trong lรบc ฤ‘ang thi. c. Cแป‘ tรฌnh lร m sai lแป‡ch ฤ‘iแปƒm trรชn bร i thi hoแบทc trong bแบฃng ฤ‘iแปƒm. d. ฤรกnh trรกo bร i thi cแปงa SV. 5. Cรกn bแป™ lร m mแบฅt bร i thi cแปงa SV khi thu bร i thi, vแบญn chuyแปƒn, bแบฃo quแบฃn, chแบฅm thi hoแบทc cรณ nhแปฏng sai phแบกm khรกc trong cรดng tรกc tแป• chแปฉc thi, tรนy theo tรญnh chแบฅt, mแปฉc ฤ‘แป™ vi phแบกm sแบฝ bแป‹ xแปญ lรฝ vi phแบกm theo mแป™t trong cรกc hรฌnh thแปฉc quy ฤ‘แป‹nh tแบกi ฤiแปu nร y. 6. Nhแปฏng cรกn bแป™, SV tuy khรดng tham gia cรดng tรกc thi nhฦฐng nแบฟu cรณ cรกc hร nh ฤ‘แป™ng tiรชu cแปฑc nhฦฐ: thi hแป™, tแป• chแปฉc lแบฅy ฤ‘แป thi ra vร  ฤ‘ฦฐa bร i giแบฃi vร o cho SV, gรขy rแป‘i lร m mแบฅt trแบฅt tแปฑ tแบกi khu vแปฑc thi sแบฝ bแป‹ buแป™c thรดi viแป‡c (nแบฟu lร  cรกn bแป™) hoแบทc buแป™c thรดi hแปc (nแบฟu lร  hแปc sinh, SV).'],
    ['Cรณ thแปƒ nhแบญn chแปฉng chแป‰ hai lแบงn trแปŸ lรชn khรดng?', 'ฤiแปu 2. Nguyรชn tแบฏc quแบฃn lรฝ, cแบฅp phรกt vฤƒn bแบฑng, chแปฉng chแป‰ 1. Vฤƒn bแบฑng, chแปฉng chแป‰ cแปงa Trฦฐแปng ฤHCNTT phรกt hร nh thuแป™c hแป‡ thแป‘ng vฤƒn bแบฑng, chแปฉng chแป‰ giรกo dแปฅc quแป‘c dรขn. 2. Vฤƒn bแบฑng, chแปฉng chแป‰ ฤ‘ฦฐแปฃc quแบฃn lรฝ thแป‘ng nhแบฅt, thแปฑc hiแป‡n phรขn cแบฅp quแบฃn lรฝ bแบฃo ฤ‘แบฃm quyแปn vร  trรกch nhiแป‡m cแปงa cรกc ฤ‘ฦกn vแป‹ trong Trฦฐแปng. 3. Vฤƒn bแบฑng, chแปฉng chแป‰ ฤ‘ฦฐแปฃc cแบฅp mแป™t lแบงn trแปซ trฦฐแปng hแปฃp quy ฤ‘แป‹nh tแบกi ฤiแปu 15 cแปงa Quy chแบฟ nร y. 4. Nghiรชm cแบฅm mแปi hร nh vi gian lแบญn trong cแบฅp phรกt vร  sแปญ dแปฅng vฤƒn bแบฑng, chแปฉng chแป‰. 5. Bแบฃo ฤ‘แบฃm cรดng khai, minh bแบกch trong cแบฅp phรกt vฤƒn bแบฑng, chแปฉng chแป‰.'],
    ['ฤฦกn vแป‹ nร o chแป‹u trรกch nhiแป‡m xแบฟp lแป‹ch thi, ฤ‘ฦฐa thรดng tin lแป‹ch thi cรกc ฤ‘แปฃt thi tแบญp trung hแป‡ ฤ‘แบกi hแปc chรญnh quy lรชn Hแป‡ thแป‘ng ?', 'V. Nแป˜I DUNG QUY TRรŒNH Quy trรฌnh phรขn cรดng cรกn bแป™ coi thi gแป“m 3 giai ฤ‘oแบกn tฦฐฦกng แปฉng vแป›i 3 quy trรฌnh thร nh phแบงn: ฤฤƒng kรฝ coi thi, Phรขn cรดng cรกn bแป™ coi thi vร  Tแป•ng kแบฟt coi thi. Chi tiแบฟt cรกc quy trรฌnh nhฦฐ sau. 1. Quy trรฌnh ฤ‘ฤƒng kรฝ coi thi Bฦฐแป›c 1: Xรขy dแปฑng kแบฟ hoแบกch thi Phรฒng ฤร o tแบกo ฤแบกi hแปc xรขy dแปฑng vร  thรดng bรกo kแบฟ hoแบกch thi cho cรกc ฤ‘ฦกn vแป‹ tแป• chแปฉc thi; trong ฤ‘รณ xรกc ฤ‘แป‹nh rรต cรกc mแป‘c thแปi gian chรญnh. Thแปi gian trฦฐแป›c 21 ngร y tรญnh tแปซ ngร y mแปŸ hแป‡ thแป‘ng ฤ‘ฤƒng kรฝ coi thi. Bฦฐแป›c 2: Xแบฟp lแป‹ch thi, ฤ‘ฦฐa lแป‹ch thi lรชn hแป‡ thแป‘ng Phรฒng ฤร o tแบกo ฤแบกi hแปc xแบฟp lแป‹ch thi, ฤ‘ฦฐa thรดng tin lแป‹ch thi lรชn Hแป‡ thแป‘ng vร  gแปญi cho cรกc ฤ‘ฦกn vแป‹ tแป• chแปฉc thi ฤ‘แปƒ chuแบฉn bแป‹ cho cรดng tรกc ฤ‘ฤƒng kรฝ coi thi. Hแป‡ thแป‘ng hแป— trแปฃ kiแปƒm tra tรญnh hแปฃp lแป‡ cแปงa lแป‹ch thi. Thแปi gian trฦฐแป›c 21 ngร y kแปƒ tแปซ ngร y mแปŸ hแป‡ thแป‘ng ฤ‘ฤƒng kรฝ coi thi. Bฦฐแป›c 3: Thรดng bรกo cรกn bแป™ vร  sinh viรชn ฤ‘ฤƒng kรฝ coi thi Phรฒng ฤร o tแบกo ฤแบกi hแปc thรดng bรกo cho cรกn bแป™ vร  sinh viรชn ฤ‘ฤƒng kรฝ coi thi qua email hoแบทc trรชn trang web Cแป•ng thรดng tin ฤ‘ร o tแบกo ฤ‘แบกi hแปc. Thแปi gian trฦฐแป›c 7 ngร y kแปƒ tแปซ ngร y mแปŸ hแป‡ thแป‘ng ฤ‘ฤƒng kรฝ coi thi. Bฦฐแป›c 4: MแปŸ hแป‡ thแป‘ng ฤ‘ฤƒng kรฝ coi thi Phรฒng ฤร o tแบกo ฤแบกi hแปc mแปŸ Hแป‡ thแป‘ng ฤ‘แปƒ cรกn bแป™, sinh viรชn ฤ‘ฤƒng kรฝ coi thi theo cรกc mแป‘c thแปi gian trong kแบฟ hoแบกch. Bฦฐแป›c 5: ฤฤƒng kรฝ coi thi Cรกn bแป™ vร  sinh viรชn ฤ‘ฤƒng kรฝ coi thi trรชn Hแป‡ thแป‘ng. Bฦฐแป›c 6: ฤรณng hแป‡ thแป‘ng ฤ‘ฤƒng kรฝ coi thi Phรฒng ฤร o tแบกo ฤแบกi hแปc ฤ‘รณng Hแป‡ thแป‘ng khi kแบฟt thรบc thแปi gian theo quy ฤ‘แป‹nh. 2. Quy trรฌnh phรขn cรดng cรกn bแป™ coi thi Bฦฐแป›c 1: Chแบกy chแปฉc nฤƒng Phรขn cรดng CBCT Phรฒng ฤร o tแบกo ฤแบกi hแปc chแบกy chแปฉc nฤƒng phรขn cรดng cรกn bแป™ coi thi cแปงa phแบงn mแปm quแบฃn lรฝ ฤ‘ร o tแบกo. Hแป‡ thแป‘ng tแปฑ ฤ‘แป™ng tรญnh ฤ‘แป‹nh mแปฉc coi thi cแปงa giแบฃng viรชn ฤ‘iแปn tรชn cรกn bแป™ chฦฐa ฤ‘ฤƒng kรฝ ฤ‘แปง sแป‘ lฦฐแปฃt coi thi theo quy ฤ‘แป‹nh vร o cรกc ca thi chฦฐa cรณ cรกn bแป™ coi thi. Thแปi gian thแปฑc hiแป‡n trฦฐแป›c 8 ngร y kแปƒ tแปซ ngร y thi ฤ‘แบงu tiรชn cแปงa ฤ‘แปฃt thi. Bฦฐแป›c 2: Thรดng bรกo CBCT kiแปƒm tra lแป‹ch coi thi Phรฒng ฤร o tแบกo ฤแบกi hแปc thรดng bรกo cรกn bแป™ coi thi kiแปƒm tra lแป‹ch coi thi tแบกi Cแป•ng thรดng tin ฤ‘ร o tแบกo ฤ‘แบกi hแปc. Thแปi gian thแปฑc hiแป‡n trฦฐแป›c 7 ngร y kแปƒ tแปซ ngร y thi ฤ‘แบงu tiรชn cแปงa ฤ‘แปฃt thi. Bฦฐแป›c 3: CBCT kiแปƒm tra lแป‹ch coi thi, ฤ‘แป xuแบฅt CBCT thay thแบฟ (nแบฟu cแบงn) Nแบฟu vรฌ lรฝ do bแบฅt khแบฃ khรกng, cรกn bแป™ coi thi khรดng thแปƒ coi thi theo lแป‹ch ฤ‘รฃ phรขn cรดng thรฌ cรกn bแป™ coi thi tรฌm ngฦฐแปi thay thแบฟ vร  sแปญ dแปฅng chแปฉc nฤƒng ฤ‘แป xuแบฅt cรกn bแป™ coi thi thay thแบฟ cแปงa Hแป‡ thแป‘ng. Sau khi ngฦฐแปi coi thi thay xรกc nhแบญn ฤ‘แป“ng รฝ coi thi thay qua Hแป‡ thแป‘ng thรฌ thay thแบฟ ฤ‘รณ mแป›i cรณ hiแป‡u lแปฑc. Nแบฟu khรดng tรฌm ฤ‘ฦฐแปฃc ngฦฐแปi thay thแบฟ cแปฅ thแปƒ thรฌ cรกn bแป™ coi thi vแบซn cรณ thแปƒ sแปญ dแปฅng chแปฉc nฤƒng ฤแป xuแบฅt cรกn bแป™ coi thi thay thแบฟ ฤ‘แปƒ tรฌm ngฦฐแปi hแป— trแปฃ coi thi, vร  phรขn cรดng ฤ‘ฦฐแปฃc thay ฤ‘แป•i khi cรณ ngฦฐแปi xรกc nhแบญn ฤ‘แป“ng รฝ coi thi thay trรชn Hแป‡ thแป‘ng. Thแปi gian thแปฑc hiแป‡n trฦฐแป›c 6 ngร y kแปƒ tแปซ ngร y thi ฤ‘แบงu tiรชn cแปงa ฤ‘แปฃt thi. Bฦฐแป›c 4: CBCT thแปฑc hiแป‡n cรดng tรกc coi thi Cรกn bแป™ coi thi cรณ trรกch nhiแป‡m thแปฑc hiแป‡n cรดng tรกc coi thi theo ฤ‘รบng lแป‹ch coi thi trรชn Hแป‡ thแป‘ng. 3. Quy trรฌnh tแป•ng kแบฟt coi thi Bฦฐแป›c 1: Khรณa Hแป‡ thแป‘ng Phรฒng ฤร o tแบกo ฤแบกi hแปc khรณa toร n bแป™ chแปฉc nฤƒng thay ฤ‘แป•i phรขn cรดng cรกn bแป™ coi thi trรชn Hแป‡ thแป‘ng. Thแปi gian hoร n thร nh lร  1 ngร y sau ngร y thi cuแป‘i cรนng cแปงa ฤ‘แปฃt thi. Bฦฐแป›c 2: Xรกc nhแบญn viแป‡c thแปฑc hiแป‡n coi thi Cรกc ฤ‘ฦกn vแป‹ tแป• chแปฉc thi xรกc nhแบญn cรกn bแป™ coi thi thแปฑc tแบฟ cho cรกc ca thi do ฤ‘ฦกn vแป‹ phแปฅ trรกch vร  gแปญi bรกo cรกo tรฌnh hรฌnh coi thi cho Ban Giรกm hiแป‡u thรดng qua Phรฒng ฤร o tแบกo ฤแบกi hแปc nแบฟu cรณ cรกn bแป™ coi thi vi phแบกm quy ฤ‘แป‹nh vแป coi thi. Thแปi gian hoร n thร nh lร  1 ngร y sau ngร y thi cuแป‘i cรนng cแปงa ฤ‘แปฃt thi. Bฦฐแป›c 3: Tแป•ng kแบฟt coi thi Trฦฐแปng xem xรฉt xแปญ lรฝ kแปท luแบญt cรกn bแป™ coi thi vi phแบกm quy ฤ‘แป‹nh vแป coi thi (nแบฟu cรณ). Phรฒng ฤร o tแบกo ฤแบกi hแปc lร m ฤ‘แป xuแบฅt, Phรฒng Kแบฟ hoแบกch Tร i chรญnh lร m thแปง tแปฅc chi trแบฃ thรน lao coi thi cho cรกn bแป™ coi thi theo quy ฤ‘แป‹nh. Thแปi gian hoร n thร nh lร  10 ngร y sau ngร y thi cuแป‘i cรนng cแปงa ฤ‘แปฃt thi.'],
    ['Sau khi buแป•i hแปc bแบฏt ฤ‘แบงu 15 phรบt nแบฟu lแป›p hแปc khรดng cรณ sinh viรชn mร  khรดng rรต lรฝ do, CBGD cรณ thแปƒ ฤ‘ฦฐแปฃc nghแป‰ khรดng?', '1. QUY ฤแปŠNH CHUNG - Phรฒng ฤร o tแบกo ฤแบกi hแปc (P. ฤTฤH) lร  ฤ‘ฦกn vแป‹ trแปฑc tiแบฟp tiแบฟp nhแบญn thรดng tin vร  xแปญ lรฝ viแป‡c bรกo thay ฤ‘แป•i liรชn quan ฤ‘แบฟn giแบฃng dแบกy (nghแป‰ dแบกy, thay ฤ‘แป•i cรกn bแป™ giแบฃng dแบกy) vร  ฤ‘ฤƒng kรฝ dแบกy bรน cแปงa cรกn bแป™ giแบฃng dแบกy. - Phรฒng Thanh tra phรกp chแบฟ vร  ฤแบฃm bแบฃo chแบฅt lฦฐแปฃng (P. TTPC&ฤBCL) lร  ฤ‘ฦกn vแป‹ phแป‘i hแปฃp vแป›i P. ฤTฤH, chแป‹u trรกch nhiแป‡m thanh tra, giรกm sรกt viแป‡c giแบฃng dแบกy; thแป‘ng kรช vร  bรกo cรกo ฤ‘แป‹nh kแปณ vแป tรฌnh hรฌnh giแบฃng dแบกy cho Ban Giรกm hiแป‡u vร  cรกc bรชn liรชn quan. - CBGD phแบฃi dแบกy ฤ‘แปง sแป‘ tiแบฟt theo qui ฤ‘แป‹nh cแปงa mรดn hแปc (qui ฤ‘แป‹nh trong ฤ‘แป cฦฐฦกng mรดn hแปc). Cรกc tiแบฟt nghแป‰ dแบกy phแบฃi ฤ‘ฦฐแปฃc ฤ‘ฤƒng kรฝ dแบกy bรน sau hoแบทc trฦฐแป›c ฤ‘รณ cho ฤ‘แบงy ฤ‘แปง. - CBGD phแบฃi bรกo cho P. ฤTฤH viแป‡c thay ฤ‘แป•i giแบฃng dแบกy (nghแป‰ dแบกy, thay ฤ‘แป•i CBGD tแบกm thแปi). Trong trฦฐแปng hแปฃp ฤ‘แป™t xuแบฅt, CBGD phแบฃi thรดng bรกo ngay cho P. ฤTฤH chแบญm nhแบฅt lร  15 phรบt sau khi buแป•i hแปc bแบฏt ฤ‘แบงu vร  gแปญi giแบฅy bรกo nghแป‰ trong vรฒng 5 ngร y sau buแป•i dแบกy ฤ‘รฃ cho nghแป‰. CBGD khรดng ฤ‘ฦฐแปฃc nghแป‰ dแบกy quรก 3 buแป•i/hแปc kแปณ/lแป›p trแปซ nhแปฏng trฦฐแปng hแปฃp nghแป‰ do ฤ‘ฦฐแปฃc ฤ‘iแปu ฤ‘แป™ng cรดng tรกc cแปงa trฦฐแปng. - Sau khi buแป•i hแปc bแบฏt ฤ‘แบงu 15 phรบt: o Nแบฟu lแป›p hแปc khรดng cรณ sinh viรชn mร  khรดng rรต lรฝ do, CBGD cรณ thแปƒ ฤ‘ฦฐแปฃc nghแป‰ sau khi ฤ‘รฃ bรกo vแป›i P.ฤTฤH vร  khรดng nhแบฅt thiแบฟt phแบฃi dแบกy bรน cho buแป•i hแปc nร y. o Nแบฟu CBGD chฦฐa ฤ‘แบฟn lแป›p mร  khรดng rรต lรฝ do thรฌ ฤ‘ฦฐแปฃc xem lร  nghแป‰ buแป•i dแบกy ฤ‘รณ vร  CBGD bแบฏt buแป™c phแบฃi tiแบฟn hร nh dแบกy bรน vร o buแป•i khรกc. Trong trฦฐแปng hแปฃp bแบฅt khแบฃ khรกng mร  CBGD ฤ‘แบฟn trแป… quรก 15 phรบt (nhฦฐng khรดng quรก 30 phรบt) thรฌ phแบฃi bรกo ngay cho P.ฤTฤH ฤ‘แปƒ thรดng bรกo cho sinh viรชn chแป, CBGD bแบฏt buแป™c tiแบฟn hร nh dแบกy bรน cho tiแบฟt hแปc ฤ‘รณ. - Trฦฐแปng hแปฃp thay ฤ‘แป•i CBGD tแบกm thแปi (CBGD bแบญn ฤ‘แป™t xuแบฅt nhฦฐng cรณ CBGD khรกc dแบกy thay): CBGD chรญnh cลฉng thแปฑc hiแป‡n tฦฐฦกng tแปฑ nhฦฐ bรกo nghแป‰ dแบกy. Sแป‘ buแป•i cรณ CBGD khรกc dแบกy thay cแปงa mแป™t lแป›p khรดng ฤ‘ฦฐแปฃc quรก 30% tแป•ng sแป‘ buแป•i dแบกy lแป›p ฤ‘รณ. - Nแบฟu CBGD dแบกy bรน trong thแปi gian thแปฑc hแปc phแบฃi ฤ‘ฤƒng kรฝ viแป‡c dแบกy bรน trฦฐแป›c ngร y dแบกy bรน รญt nhแบฅt lร  5 ngร y vร  buแป•i dแบกy bรน khรดng ฤ‘ฦฐแปฃc cรนng ngร y vแป›i buแป•i hแปc chรญnh theo thแปi khรณa biแปƒu cแปงa lแป›p. - P. ฤTฤH cรณ thแปƒ yรชu cแบงu CBGD ฤ‘แป•i buแป•i dแบกy bรน nแบฟu thแปi gian mร  CBGD ฤ‘ฤƒng kรฝ khรดng phรน hแปฃp vแป›i lแป‹ch hแปc cแปงa sinh viรชn (cรณ trรชn 30% sinh viรชn cแปงa lแป›p cรณ lแป‹ch hแปc khรกc cแปงa trฦฐแปng vร o thแปi gian CBGD ฤ‘ฤƒng kรฝ dแบกy bรน).'],
    ['Hแปc phรญ cแปงa chฦฐฦกng trรฌnh ฤ‘ร o tแบกo tแปซ xa ฤ‘ฦฐแปฃc tรญnh nhฦฐ thแบฟ nร o?', 'ฤiแปu 10. Nguyรชn tแบฏc xรกc ฤ‘แป‹nh hแปc phรญ Hแปc phรญ ฤ‘ฦฐแปฃc thu theo tแปซng hแปc kแปณ vร  ฤ‘ฦฐแปฃc tรญnh theo cรดng thแปฉc sau: Hแปc phรญ = HPTCHM ร— TCHPHM + HPTCHL ร— TCHPHL + HPTCCT ร— TCHPCT Trong ฤ‘รณ: - HPTCHM: mแปฉc hแปc phรญ cho loแบกi tรญn chแป‰ hแปc phรญ hแปc mแป›i. - TCHPHM: tแป•ng sแป‘ tรญn chแป‰ hแปc phรญ cแปงa cรกc mรดn hแปc mแป›i. - HPTCHL: mแปฉc hแปc phรญ cho loแบกi tรญn chแป‰ hแปc phรญ hแปc lแบกi. - TCHPHL: tแป•ng sแป‘ tรญn chแป‰ hแปc phรญ cแปงa cรกc mรดn hแปc lแบกi. - HPTCCT: mแปฉc hแปc phรญ cho loแบกi tรญn chแป‰ hแปc phรญ hแปc cแบฃi thiแป‡n. - TCHPCT: tแป•ng sแป‘ tรญn chแป‰ hแปc phรญ cแปงa cรกc mรดn hแปc cแบฃi thiแป‡n Vร o ฤ‘แบงu khรณa hแปc hoแบทc nฤƒm hแปc, Trฦฐแปng cรดng bแป‘ mแปฉc hแปc phรญ cho mแป—i loแบกi tรญn chแป‰ hแปc phรญ.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'Trฦฐแปng hแปฃp mแป™t SV ฤ‘ฦฐแปฃc CBCT cho xem bร i cแปงa SV khรกc trong giแป thi ฤ‘ฦฐแปฃc giแบฃi quyแบฟt nhฦฐ thแบฟ nร o?',
    [
        'ฤiแปu 22. Xแปญ lรฝ cรกn bแป™ vi phแบกm quy ฤ‘แป‹nh Trong thแปi gian thi, nแบฟu cรกc cรกn bแป™ thanh tra thi phรกt hiแป‡n thแบฅy cรกc trฦฐแปng hแปฃp vi phแบกm thรฌ lแบญp biรชn bแบฃn tแบกi chแป— vร  ฤ‘แป nghแป‹ Hiแป‡u trฦฐแปŸng xแปญ lรฝ. Ngฦฐแปi tham gia cรดng tรกc thi cรณ hร nh vi vi phแบกm quy ฤ‘แป‹nh (bแป‹ phรกt hiแป‡n trong khi lร m nhiแป‡m vแปฅ hoแบทc sau kแปณ thi), nแบฟu cรณ ฤ‘แปง chแปฉng cแปฉ, tรนy theo mแปฉc ฤ‘แป™, sแบฝ bแป‹ xแปญ lรฝ theo cรกc hรฌnh thแปฉc sau ฤ‘รขy. 1. Khiแปƒn trรกch ฤ‘แป‘i vแป›i nhแปฏng ngฦฐแปi phแบกm lแป—i nhแบน trong khi thi hร nh nhiแป‡m vแปฅ. 2. Cแบฃnh cรกo ฤ‘แป‘i vแป›i nhแปฏng ngฦฐแปi vi phแบกm mแป™t trong cรกc lแป—i sau ฤ‘รขy: a. CBCT bแป coi thi mร  khรดng thรดng bรกo cho ฤ‘ฦกn vแป‹ tแป• chแปฉc mรดn thi tแปซ 2 lแบงn trแปŸ lรชn trong mแป™t ฤ‘แปฃt thi. b. CBCT ฤ‘แปƒ cho SV tแปฑ do trao ฤ‘แป•i khi lร m bร i, sแปญ dแปฅng tร i liแป‡u bแป‹ cแบฅm sแปญ dแปฅng hoแบทc sแปญ dแปฅng cรกc phฦฐฦกng tiแป‡n thรดng tin liรชn lแบกc tแบกi phรฒng thi. c. Cรกn bแป™ chแบฅm thi chแบฅm ฤ‘iแปƒm hoแบทc cแป™ng ฤ‘iแปƒm bร i thi cรณ nhiแปu sai sรณt (trรชn 5% tแป•ng sแป‘ bร i thi). d. Cรกn bแป™ chแบฅm thi nแป™p ฤ‘iแปƒm trแป… so vแป›i thแปi hแบกn quy ฤ‘แป‹nh. 3. Tรนy theo mแปฉc ฤ‘แป™ vi phแบกm cรณ thแปƒ bแป‹ hแบก bแบญc lฦฐฦกng, hแบก ngแบกch, cรกch chแปฉc hoแบทc chuyแปƒn ฤ‘i lร m cรดng tรกc khรกc ฤ‘แป‘i vแป›i nhแปฏng ngฦฐแปi vi phแบกm mแป™t trong cรกc lแป—i sau ฤ‘รขy: a. Trแปฑc tiแบฟp giแบฃi bร i rแป“i hฦฐแป›ng dแบซn cho SV lรบc ฤ‘ang thi. b. Lแบฅy bร i thi cแปงa SV lร m ฤ‘ฦฐแปฃc giao cho SV khรกc. 4. Buแป™c thรดi viแป‡c hoแบทc bแป‹ xแปญ lรฝ theo phรกp luแบญt ฤ‘แป‘i vแป›i ngฦฐแปi cรณ mแป™t trong cรกc hร nh vi sai phแบกm sau ฤ‘รขy: a. Lร m lแป™ ฤ‘แป thi, mua, bรกn ฤ‘แป thi. b. ฤฦฐa ฤ‘แป thi ra ngoร i khu vแปฑc thi hoแบทc ฤ‘ฦฐa bร i giแบฃi tแปซ ngoร i vร o phรฒng thi trong lรบc ฤ‘ang thi. c. Cแป‘ tรฌnh lร m sai lแป‡ch ฤ‘iแปƒm trรชn bร i thi hoแบทc trong bแบฃng ฤ‘iแปƒm. d. ฤรกnh trรกo bร i thi cแปงa SV. 5. Cรกn bแป™ lร m mแบฅt bร i thi cแปงa SV khi thu bร i thi, vแบญn chuyแปƒn, bแบฃo quแบฃn, chแบฅm thi hoแบทc cรณ nhแปฏng sai phแบกm khรกc trong cรดng tรกc tแป• chแปฉc thi, tรนy theo tรญnh chแบฅt, mแปฉc ฤ‘แป™ vi phแบกm sแบฝ bแป‹ xแปญ lรฝ vi phแบกm theo mแป™t trong cรกc hรฌnh thแปฉc quy ฤ‘แป‹nh tแบกi ฤiแปu nร y. 6. Nhแปฏng cรกn bแป™, SV tuy khรดng tham gia cรดng tรกc thi nhฦฐng nแบฟu cรณ cรกc hร nh ฤ‘แป™ng tiรชu cแปฑc nhฦฐ: thi hแป™, tแป• chแปฉc lแบฅy ฤ‘แป thi ra vร  ฤ‘ฦฐa bร i giแบฃi vร o cho SV, gรขy rแป‘i lร m mแบฅt trแบฅt tแปฑ tแบกi khu vแปฑc thi sแบฝ bแป‹ buแป™c thรดi viแป‡c (nแบฟu lร  cรกn bแป™) hoแบทc buแป™c thรดi hแปc (nแบฟu lร  hแปc sinh, SV).',
        'ฤiแปu 2. Nguyรชn tแบฏc quแบฃn lรฝ, cแบฅp phรกt vฤƒn bแบฑng, chแปฉng chแป‰ 1. Vฤƒn bแบฑng, chแปฉng chแป‰ cแปงa Trฦฐแปng ฤHCNTT phรกt hร nh thuแป™c hแป‡ thแป‘ng vฤƒn bแบฑng, chแปฉng chแป‰ giรกo dแปฅc quแป‘c dรขn. 2. Vฤƒn bแบฑng, chแปฉng chแป‰ ฤ‘ฦฐแปฃc quแบฃn lรฝ thแป‘ng nhแบฅt, thแปฑc hiแป‡n phรขn cแบฅp quแบฃn lรฝ bแบฃo ฤ‘แบฃm quyแปn vร  trรกch nhiแป‡m cแปงa cรกc ฤ‘ฦกn vแป‹ trong Trฦฐแปng. 3. Vฤƒn bแบฑng, chแปฉng chแป‰ ฤ‘ฦฐแปฃc cแบฅp mแป™t lแบงn trแปซ trฦฐแปng hแปฃp quy ฤ‘แป‹nh tแบกi ฤiแปu 15 cแปงa Quy chแบฟ nร y. 4. Nghiรชm cแบฅm mแปi hร nh vi gian lแบญn trong cแบฅp phรกt vร  sแปญ dแปฅng vฤƒn bแบฑng, chแปฉng chแป‰. 5. Bแบฃo ฤ‘แบฃm cรดng khai, minh bแบกch trong cแบฅp phรกt vฤƒn bแบฑng, chแปฉng chแป‰.',
        'V. Nแป˜I DUNG QUY TRรŒNH Quy trรฌnh phรขn cรดng cรกn bแป™ coi thi gแป“m 3 giai ฤ‘oแบกn tฦฐฦกng แปฉng vแป›i 3 quy trรฌnh thร nh phแบงn: ฤฤƒng kรฝ coi thi, Phรขn cรดng cรกn bแป™ coi thi vร  Tแป•ng kแบฟt coi thi. Chi tiแบฟt cรกc quy trรฌnh nhฦฐ sau. 1. Quy trรฌnh ฤ‘ฤƒng kรฝ coi thi Bฦฐแป›c 1: Xรขy dแปฑng kแบฟ hoแบกch thi Phรฒng ฤร o tแบกo ฤแบกi hแปc xรขy dแปฑng vร  thรดng bรกo kแบฟ hoแบกch thi cho cรกc ฤ‘ฦกn vแป‹ tแป• chแปฉc thi; trong ฤ‘รณ xรกc ฤ‘แป‹nh rรต cรกc mแป‘c thแปi gian chรญnh. Thแปi gian trฦฐแป›c 21 ngร y tรญnh tแปซ ngร y mแปŸ hแป‡ thแป‘ng ฤ‘ฤƒng kรฝ coi thi. Bฦฐแป›c 2: Xแบฟp lแป‹ch thi, ฤ‘ฦฐa lแป‹ch thi lรชn hแป‡ thแป‘ng Phรฒng ฤร o tแบกo ฤแบกi hแปc xแบฟp lแป‹ch thi, ฤ‘ฦฐa thรดng tin lแป‹ch thi lรชn Hแป‡ thแป‘ng vร  gแปญi cho cรกc ฤ‘ฦกn vแป‹ tแป• chแปฉc thi ฤ‘แปƒ chuแบฉn bแป‹ cho cรดng tรกc ฤ‘ฤƒng kรฝ coi thi. Hแป‡ thแป‘ng hแป— trแปฃ kiแปƒm tra tรญnh hแปฃp lแป‡ cแปงa lแป‹ch thi. Thแปi gian trฦฐแป›c 21 ngร y kแปƒ tแปซ ngร y mแปŸ hแป‡ thแป‘ng ฤ‘ฤƒng kรฝ coi thi. Bฦฐแป›c 3: Thรดng bรกo cรกn bแป™ vร  sinh viรชn ฤ‘ฤƒng kรฝ coi thi Phรฒng ฤร o tแบกo ฤแบกi hแปc thรดng bรกo cho cรกn bแป™ vร  sinh viรชn ฤ‘ฤƒng kรฝ coi thi qua email hoแบทc trรชn trang web Cแป•ng thรดng tin ฤ‘ร o tแบกo ฤ‘แบกi hแปc. Thแปi gian trฦฐแป›c 7 ngร y kแปƒ tแปซ ngร y mแปŸ hแป‡ thแป‘ng ฤ‘ฤƒng kรฝ coi thi. Bฦฐแป›c 4: MแปŸ hแป‡ thแป‘ng ฤ‘ฤƒng kรฝ coi thi Phรฒng ฤร o tแบกo ฤแบกi hแปc mแปŸ Hแป‡ thแป‘ng ฤ‘แปƒ cรกn bแป™, sinh viรชn ฤ‘ฤƒng kรฝ coi thi theo cรกc mแป‘c thแปi gian trong kแบฟ hoแบกch. Bฦฐแป›c 5: ฤฤƒng kรฝ coi thi Cรกn bแป™ vร  sinh viรชn ฤ‘ฤƒng kรฝ coi thi trรชn Hแป‡ thแป‘ng. Bฦฐแป›c 6: ฤรณng hแป‡ thแป‘ng ฤ‘ฤƒng kรฝ coi thi Phรฒng ฤร o tแบกo ฤแบกi hแปc ฤ‘รณng Hแป‡ thแป‘ng khi kแบฟt thรบc thแปi gian theo quy ฤ‘แป‹nh. 2. Quy trรฌnh phรขn cรดng cรกn bแป™ coi thi Bฦฐแป›c 1: Chแบกy chแปฉc nฤƒng Phรขn cรดng CBCT Phรฒng ฤร o tแบกo ฤแบกi hแปc chแบกy chแปฉc nฤƒng phรขn cรดng cรกn bแป™ coi thi cแปงa phแบงn mแปm quแบฃn lรฝ ฤ‘ร o tแบกo. Hแป‡ thแป‘ng tแปฑ ฤ‘แป™ng tรญnh ฤ‘แป‹nh mแปฉc coi thi cแปงa giแบฃng viรชn ฤ‘iแปn tรชn cรกn bแป™ chฦฐa ฤ‘ฤƒng kรฝ ฤ‘แปง sแป‘ lฦฐแปฃt coi thi theo quy ฤ‘แป‹nh vร o cรกc ca thi chฦฐa cรณ cรกn bแป™ coi thi. Thแปi gian thแปฑc hiแป‡n trฦฐแป›c 8 ngร y kแปƒ tแปซ ngร y thi ฤ‘แบงu tiรชn cแปงa ฤ‘แปฃt thi. Bฦฐแป›c 2: Thรดng bรกo CBCT kiแปƒm tra lแป‹ch coi thi Phรฒng ฤร o tแบกo ฤแบกi hแปc thรดng bรกo cรกn bแป™ coi thi kiแปƒm tra lแป‹ch coi thi tแบกi Cแป•ng thรดng tin ฤ‘ร o tแบกo ฤ‘แบกi hแปc. Thแปi gian thแปฑc hiแป‡n trฦฐแป›c 7 ngร y kแปƒ tแปซ ngร y thi ฤ‘แบงu tiรชn cแปงa ฤ‘แปฃt thi. Bฦฐแป›c 3: CBCT kiแปƒm tra lแป‹ch coi thi, ฤ‘แป xuแบฅt CBCT thay thแบฟ (nแบฟu cแบงn) Nแบฟu vรฌ lรฝ do bแบฅt khแบฃ khรกng, cรกn bแป™ coi thi khรดng thแปƒ coi thi theo lแป‹ch ฤ‘รฃ phรขn cรดng thรฌ cรกn bแป™ coi thi tรฌm ngฦฐแปi thay thแบฟ vร  sแปญ dแปฅng chแปฉc nฤƒng ฤ‘แป xuแบฅt cรกn bแป™ coi thi thay thแบฟ cแปงa Hแป‡ thแป‘ng. Sau khi ngฦฐแปi coi thi thay xรกc nhแบญn ฤ‘แป“ng รฝ coi thi thay qua Hแป‡ thแป‘ng thรฌ thay thแบฟ ฤ‘รณ mแป›i cรณ hiแป‡u lแปฑc. Nแบฟu khรดng tรฌm ฤ‘ฦฐแปฃc ngฦฐแปi thay thแบฟ cแปฅ thแปƒ thรฌ cรกn bแป™ coi thi vแบซn cรณ thแปƒ sแปญ dแปฅng chแปฉc nฤƒng ฤแป xuแบฅt cรกn bแป™ coi thi thay thแบฟ ฤ‘แปƒ tรฌm ngฦฐแปi hแป— trแปฃ coi thi, vร  phรขn cรดng ฤ‘ฦฐแปฃc thay ฤ‘แป•i khi cรณ ngฦฐแปi xรกc nhแบญn ฤ‘แป“ng รฝ coi thi thay trรชn Hแป‡ thแป‘ng. Thแปi gian thแปฑc hiแป‡n trฦฐแป›c 6 ngร y kแปƒ tแปซ ngร y thi ฤ‘แบงu tiรชn cแปงa ฤ‘แปฃt thi. Bฦฐแป›c 4: CBCT thแปฑc hiแป‡n cรดng tรกc coi thi Cรกn bแป™ coi thi cรณ trรกch nhiแป‡m thแปฑc hiแป‡n cรดng tรกc coi thi theo ฤ‘รบng lแป‹ch coi thi trรชn Hแป‡ thแป‘ng. 3. Quy trรฌnh tแป•ng kแบฟt coi thi Bฦฐแป›c 1: Khรณa Hแป‡ thแป‘ng Phรฒng ฤร o tแบกo ฤแบกi hแปc khรณa toร n bแป™ chแปฉc nฤƒng thay ฤ‘แป•i phรขn cรดng cรกn bแป™ coi thi trรชn Hแป‡ thแป‘ng. Thแปi gian hoร n thร nh lร  1 ngร y sau ngร y thi cuแป‘i cรนng cแปงa ฤ‘แปฃt thi. Bฦฐแป›c 2: Xรกc nhแบญn viแป‡c thแปฑc hiแป‡n coi thi Cรกc ฤ‘ฦกn vแป‹ tแป• chแปฉc thi xรกc nhแบญn cรกn bแป™ coi thi thแปฑc tแบฟ cho cรกc ca thi do ฤ‘ฦกn vแป‹ phแปฅ trรกch vร  gแปญi bรกo cรกo tรฌnh hรฌnh coi thi cho Ban Giรกm hiแป‡u thรดng qua Phรฒng ฤร o tแบกo ฤแบกi hแปc nแบฟu cรณ cรกn bแป™ coi thi vi phแบกm quy ฤ‘แป‹nh vแป coi thi. Thแปi gian hoร n thร nh lร  1 ngร y sau ngร y thi cuแป‘i cรนng cแปงa ฤ‘แปฃt thi. Bฦฐแป›c 3: Tแป•ng kแบฟt coi thi Trฦฐแปng xem xรฉt xแปญ lรฝ kแปท luแบญt cรกn bแป™ coi thi vi phแบกm quy ฤ‘แป‹nh vแป coi thi (nแบฟu cรณ). Phรฒng ฤร o tแบกo ฤแบกi hแปc lร m ฤ‘แป xuแบฅt, Phรฒng Kแบฟ hoแบกch Tร i chรญnh lร m thแปง tแปฅc chi trแบฃ thรน lao coi thi cho cรกn bแป™ coi thi theo quy ฤ‘แป‹nh. Thแปi gian hoร n thร nh lร  10 ngร y sau ngร y thi cuแป‘i cรนng cแปงa ฤ‘แปฃt thi.',
        '1. QUY ฤแปŠNH CHUNG - Phรฒng ฤร o tแบกo ฤแบกi hแปc (P. ฤTฤH) lร  ฤ‘ฦกn vแป‹ trแปฑc tiแบฟp tiแบฟp nhแบญn thรดng tin vร  xแปญ lรฝ viแป‡c bรกo thay ฤ‘แป•i liรชn quan ฤ‘แบฟn giแบฃng dแบกy (nghแป‰ dแบกy, thay ฤ‘แป•i cรกn bแป™ giแบฃng dแบกy) vร  ฤ‘ฤƒng kรฝ dแบกy bรน cแปงa cรกn bแป™ giแบฃng dแบกy. - Phรฒng Thanh tra phรกp chแบฟ vร  ฤแบฃm bแบฃo chแบฅt lฦฐแปฃng (P. TTPC&ฤBCL) lร  ฤ‘ฦกn vแป‹ phแป‘i hแปฃp vแป›i P. ฤTฤH, chแป‹u trรกch nhiแป‡m thanh tra, giรกm sรกt viแป‡c giแบฃng dแบกy; thแป‘ng kรช vร  bรกo cรกo ฤ‘แป‹nh kแปณ vแป tรฌnh hรฌnh giแบฃng dแบกy cho Ban Giรกm hiแป‡u vร  cรกc bรชn liรชn quan. - CBGD phแบฃi dแบกy ฤ‘แปง sแป‘ tiแบฟt theo qui ฤ‘แป‹nh cแปงa mรดn hแปc (qui ฤ‘แป‹nh trong ฤ‘แป cฦฐฦกng mรดn hแปc). Cรกc tiแบฟt nghแป‰ dแบกy phแบฃi ฤ‘ฦฐแปฃc ฤ‘ฤƒng kรฝ dแบกy bรน sau hoแบทc trฦฐแป›c ฤ‘รณ cho ฤ‘แบงy ฤ‘แปง. - CBGD phแบฃi bรกo cho P. ฤTฤH viแป‡c thay ฤ‘แป•i giแบฃng dแบกy (nghแป‰ dแบกy, thay ฤ‘แป•i CBGD tแบกm thแปi). Trong trฦฐแปng hแปฃp ฤ‘แป™t xuแบฅt, CBGD phแบฃi thรดng bรกo ngay cho P. ฤTฤH chแบญm nhแบฅt lร  15 phรบt sau khi buแป•i hแปc bแบฏt ฤ‘แบงu vร  gแปญi giแบฅy bรกo nghแป‰ trong vรฒng 5 ngร y sau buแป•i dแบกy ฤ‘รฃ cho nghแป‰. CBGD khรดng ฤ‘ฦฐแปฃc nghแป‰ dแบกy quรก 3 buแป•i/hแปc kแปณ/lแป›p trแปซ nhแปฏng trฦฐแปng hแปฃp nghแป‰ do ฤ‘ฦฐแปฃc ฤ‘iแปu ฤ‘แป™ng cรดng tรกc cแปงa trฦฐแปng. - Sau khi buแป•i hแปc bแบฏt ฤ‘แบงu 15 phรบt: o Nแบฟu lแป›p hแปc khรดng cรณ sinh viรชn mร  khรดng rรต lรฝ do, CBGD cรณ thแปƒ ฤ‘ฦฐแปฃc nghแป‰ sau khi ฤ‘รฃ bรกo vแป›i P.ฤTฤH vร  khรดng nhแบฅt thiแบฟt phแบฃi dแบกy bรน cho buแป•i hแปc nร y. o Nแบฟu CBGD chฦฐa ฤ‘แบฟn lแป›p mร  khรดng rรต lรฝ do thรฌ ฤ‘ฦฐแปฃc xem lร  nghแป‰ buแป•i dแบกy ฤ‘รณ vร  CBGD bแบฏt buแป™c phแบฃi tiแบฟn hร nh dแบกy bรน vร o buแป•i khรกc. Trong trฦฐแปng hแปฃp bแบฅt khแบฃ khรกng mร  CBGD ฤ‘แบฟn trแป… quรก 15 phรบt (nhฦฐng khรดng quรก 30 phรบt) thรฌ phแบฃi bรกo ngay cho P.ฤTฤH ฤ‘แปƒ thรดng bรกo cho sinh viรชn chแป, CBGD bแบฏt buแป™c tiแบฟn hร nh dแบกy bรน cho tiแบฟt hแปc ฤ‘รณ. - Trฦฐแปng hแปฃp thay ฤ‘แป•i CBGD tแบกm thแปi (CBGD bแบญn ฤ‘แป™t xuแบฅt nhฦฐng cรณ CBGD khรกc dแบกy thay): CBGD chรญnh cลฉng thแปฑc hiแป‡n tฦฐฦกng tแปฑ nhฦฐ bรกo nghแป‰ dแบกy. Sแป‘ buแป•i cรณ CBGD khรกc dแบกy thay cแปงa mแป™t lแป›p khรดng ฤ‘ฦฐแปฃc quรก 30% tแป•ng sแป‘ buแป•i dแบกy lแป›p ฤ‘รณ. - Nแบฟu CBGD dแบกy bรน trong thแปi gian thแปฑc hแปc phแบฃi ฤ‘ฤƒng kรฝ viแป‡c dแบกy bรน trฦฐแป›c ngร y dแบกy bรน รญt nhแบฅt lร  5 ngร y vร  buแป•i dแบกy bรน khรดng ฤ‘ฦฐแปฃc cรนng ngร y vแป›i buแป•i hแปc chรญnh theo thแปi khรณa biแปƒu cแปงa lแป›p. - P. ฤTฤH cรณ thแปƒ yรชu cแบงu CBGD ฤ‘แป•i buแป•i dแบกy bรน nแบฟu thแปi gian mร  CBGD ฤ‘ฤƒng kรฝ khรดng phรน hแปฃp vแป›i lแป‹ch hแปc cแปงa sinh viรชn (cรณ trรชn 30% sinh viรชn cแปงa lแป›p cรณ lแป‹ch hแปc khรกc cแปงa trฦฐแปng vร o thแปi gian CBGD ฤ‘ฤƒng kรฝ dแบกy bรน).',
        'ฤiแปu 10. Nguyรชn tแบฏc xรกc ฤ‘แป‹nh hแปc phรญ Hแปc phรญ ฤ‘ฦฐแปฃc thu theo tแปซng hแปc kแปณ vร  ฤ‘ฦฐแปฃc tรญnh theo cรดng thแปฉc sau: Hแปc phรญ = HPTCHM ร— TCHPHM + HPTCHL ร— TCHPHL + HPTCCT ร— TCHPCT Trong ฤ‘รณ: - HPTCHM: mแปฉc hแปc phรญ cho loแบกi tรญn chแป‰ hแปc phรญ hแปc mแป›i. - TCHPHM: tแป•ng sแป‘ tรญn chแป‰ hแปc phรญ cแปงa cรกc mรดn hแปc mแป›i. - HPTCHL: mแปฉc hแปc phรญ cho loแบกi tรญn chแป‰ hแปc phรญ hแปc lแบกi. - TCHPHL: tแป•ng sแป‘ tรญn chแป‰ hแปc phรญ cแปงa cรกc mรดn hแปc lแบกi. - HPTCCT: mแปฉc hแปc phรญ cho loแบกi tรญn chแป‰ hแปc phรญ hแปc cแบฃi thiแป‡n. - TCHPCT: tแป•ng sแป‘ tรญn chแป‰ hแปc phรญ cแปงa cรกc mรดn hแปc cแบฃi thiแป‡n Vร o ฤ‘แบงu khรณa hแปc hoแบทc nฤƒm hแปc, Trฦฐแปng cรดng bแป‘ mแปฉc hแปc phรญ cho mแป—i loแบกi tรญn chแป‰ hแปc phรญ.',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Training Details

Training Dataset

Unnamed Dataset

Customize original dataset UIT-R2GQA using function mine_hard_negatives

  • Size: 7,806 training samples
  • Columns: question, context, and negative
  • Approximate statistics based on the first 1000 samples:
    question context negative
    type string string string
    details
    • min: 10 characters
    • mean: 75.23 characters
    • max: 242 characters
    • min: 99 characters
    • mean: 1764.58 characters
    • max: 5480 characters
    • min: 75 characters
    • mean: 1177.52 characters
    • max: 5480 characters
  • Samples:
    question context negative
    Yรชu cแบงu cแปงa giรกo trรฌnh ฤ‘ฦฐแปฃc sแปญ dแปฅng cho viแป‡c giแบฃng dแบกy lร  gรฌ? ฤiแปu 5. Giรกo trรฌnh cho mแป—i hแปc phแบงn 5.1. Mรดฬƒi hoฬฃc phแบงn dรนng mแปฬ‚t giรกo trรฌnh chรญnh. Trong tฦฐฬ€ng giai ฤ‘oaฬฃn, giรกo trรฌnh cho hoฬฃc phแบงn cรณ thรชฬ‰ thay ฤ‘รดฬ‰i do Hiแป‡u trฦฐแปŸng ra quyรชฬt ฤ‘iฬฃnh. 5.2. Ngoร i mแปฬ‚t giรกo trรฌnh chรญnh, mรดฬƒi hoฬฃc phแบงn ฤ‘uฬ›ฦกฬฃc trฦฐแปng tรดฬ‰ chฦฐฬc biรชn soaฬฃn tรดฬi ฤ‘a hai sรกch chuyรชn khaฬ‰o, ba tร i liแบนฬ‚u tham khaฬ‰o, mแปฬ‚t tร i liแบนฬ‚u huฬ›ฦกฬng dรขฬƒn. 5.3. Cรกc giรกo trรฌnh sฦฐฬ‰ duฬฃng trong giaฬ‰ng daฬฃy phaฬ‰i ฤ‘uฬ›ฦกฬฃc ghi rรต trong ฤ‘รชฬ€ cuฬ›oฬ›ng hoฬฃc phแบงn ฤ‘รฃ ฤ‘uฬ›ฦกฬฃc Hiแป‡u trฦฐแปŸng phรช duyแบนฬ‚t. ฤiแปu 13. Kinh phiฬ giaฬo triฬ€nh ฤHQG hรดฬƒ trฦกฬฃ cho xuรขฬt baฬ‰n giaฬo triฬ€nh 13.1. ฤรดฬi vฦกฬi giaฬo triฬ€nh xuรขฬt baฬ‰n ฤรชฬ‰ thuฬc ฤ‘รขฬ‰y cho coฬ‚ng taฬc giaฬo triฬ€nh phaฬt triรชฬ‰n nhanh, ฤ‘aฬp ฦฐฬng kiฬฃp cho nhu cรขฬ€u ฤ‘aฬ€o taฬฃo, ฤHQG chi hรดฬƒ trฦกฬฃ mแปฬ‚t phรขฬ€n kinh phiฬ. Cuฬฃ thรชฬ‰ laฬ€: a) Hรดฬƒ trฦกฬฃ 50% cho phiฬ in ฤ‘รดฬi vฦกฬi giaฬo triฬ€nh coฬ sรดฬ luฬ›ฦกฬฃng xuรขฬt baฬ‰n ฤ‘รชฬn 500 quyรชฬ‰n. Cuฬฃ thรชฬ‰, mฦฐฬc hรดฬƒ trฦกฬฃ kinh phiฬ in 1 tฦฐฬฃa giaฬo triฬ€nh ฤ‘uฬ›ฦกฬฃc tiฬnh nhuฬ› sau: = (ฤ‘oฬ›n giaฬ in 1 trang ร— sรดฬ trang ร— sรดฬ luฬ›ฦกฬฃng giaฬo triฬ€nh xuรขฬt baฬ‰n) ร— 50% b) Hรดฬƒ trฦกฬฃ 100% phiฬ quaฬ‰n lyฬ vaฬ€ phiฬ bieฬ‚n tแบกฬ‚p cuฬ‰a NXB ฤHQG. 13.2. ฤรดฬi vฦกฬi giaฬo triฬ€nh taฬi baฬ‰n Giaฬo triฬ€nh taฬi baฬ‰n ฤ‘uฬ›ฦกฬฃc hรดฬƒ trฦกฬฃ kinh phiฬ theo mฦฐฬc ghi ฦกฬ‰ muฬฃc a, b khoaฬ‰n 1 ฤiรชฬ€u naฬ€y.
    Nhแปฏng cรก nhรขn hแปc ฤ‘แบกi hแปc hay thแบกc sฤฉ hแป‡ ฤ‘ร o tแบกo tแปซ xa cรณ liรชn quan ฤ‘แบฟn quy chแบฟ ฤ‘ร o tแบกo tแปซ xa? ฤiแปu 1. Phแบกm vi ฤ‘iแปu chแป‰nh vร  ฤ‘แป‘i tฦฐแปฃng รกp dแปฅng 1. Quy chแบฟ nร y quy ฤ‘แป‹nh nhแปฏng ฤ‘iแปu chung nhแบฅt vแป tแป• chแปฉc tuyแปƒn sinh vร  ฤ‘ร o tแบกo theo hรฌnh thแปฉc ฤ‘ร o tแบกo tแปซ xa (hay ฤ‘ร o tแบกo tแปซ xa qua mแบกng) trรฌnh ฤ‘แป™ ฤ‘แบกi hแปc cแปงa Trฦฐแปng ฤแบกi hแปc Cรดng nghแป‡ Thรดng tin (gแปi tแบฏt lร  Trฦฐแปng) thuแป™c ฤHQG-HCM. 2. Quy chแบฟ nร y รกp dแปฅng ฤ‘แป‘i vแป›i sinh viรชn, giแบฃng viรชn, cรกc ฤ‘ฦกn vแป‹ vร  cรก nhรขn cรณ liรชn quan ฤ‘แบฟn ฤ‘ร o tแบกo tแปซ xa trรฌnh ฤ‘แป™ ฤ‘แบกi hแปc cแปงa Trฦฐแปng. ฤiแปu 1. Phแบกm vi ฤ‘iแปu chแป‰nh vร  ฤ‘แป‘i tฦฐแปฃng รกp dแปฅng 1. Vฤƒn bแบฃn nร y quy ฤ‘แป‹nh vแป tuyแปƒn sinh, tแป• chแปฉc vร  quแบฃn lรฝ ฤ‘ร o tแบกo ฤ‘แป‘i vแป›i chฦฐฦกng trรฌnh ฤ‘ร o tแบกo liรชn thรดng tแปซ trรฌnh ฤ‘แป™ ฤ‘แบกi hแปc lรชn trรฌnh ฤ‘รด thแบกc sฤฉ hแป‡ chรญnh quy cแปงa Trฦฐแปng ฤแบกi hแปc Cรดng nghแป‡ Thรดng tin โ€“ ฤHQG-HCM (sau ฤ‘รขy gแปi tแบฏt lร  chฦฐฦกng trรฌnh ฤH-ThS). 2. Quy ฤ‘แป‹nh nร y รกp dแปฅng ฤ‘แป‘i vแป›i tแป• chแปฉc vร  cรก nhรขn cรณ liรชn quan ฤ‘แบฟn chฦฐฦกng trรฌnh ฤH-ThS.
    Cรกc buแป•i seminar ngoแบกi khรณa ฤ‘ฦฐแปฃc tแป• chแปฉc nhฦฐ thแบฟ nร o? ฤiแปu 2. Giแบฃi thรญch tแปซ ngแปฏ Sinh viรชn thuแป™c chฦฐฦกng trรฌnh tร i nฤƒng cรณ 02 hรฌnh thแปฉc lร  chรญnh thแปฉc vร  dแปฑ bแป‹, nแบฑm trong tแป•ng chแป‰ tiรชu cแปงa chฦฐฦกng trรฌnh: - Sinh viรชn chรญnh thแปฉc: ฤ‘แบฃm bแบฃo ฤ‘รกp แปฉng cรกc tiรชu chuแบฉn theo yรชu cแบงu vร  ฤ‘ฦฐแปฃc hฦฐแปŸng ฤ‘แบงy ฤ‘แปง cรกc quyแปn lแปฃi cแปงa chฦฐฦกng trรฌnh. - Sinh viรชn dแปฑ bแป‹: cรนng ฤ‘iแปu kiแป‡n hแปc tแบญp vร  hแปc cรนng chฦฐฦกng trรฌnh ฤ‘ร o tแบกo vแป›i sinh viรชn chรญnh thแปฉc nhฦฐng khรดng ฤ‘ฦฐแปฃc hฦฐแปŸng chรญnh sรกch vแป hแปc bแป•ng tร i nฤƒng. Sinh viรชn dแปฑ bแป‹ bao gแป“m cรกc sinh viรชn trong lแป›p tร i nฤƒng vi phแบกm ฤ‘iแปu kiแป‡n ฤ‘แบกt sinh viรชn chรญnh thแปฉc nhฦฐng chฦฐa bแป‹ loแบกi ra khแปi chฦฐฦกng trรฌnh trong cรกc ฤ‘แปฃt xรฉt sau mแป—i hแปc kแปณ, vร  sinh viรชn mแป›i tuyแปƒn bแป• sung vร o chฦฐฦกng trรฌnh. Mรดn hแปc cแป‘t lรตi: lร  mรดn hแปc quan trแปng trong CTฤT, lร  phแบงn kiแบฟn thแปฉc khรดng thแปƒ thiแบฟu trong lฤฉnh vแปฑc chuyรชn mรดn liรชn quan tแป›i ngร nh ฤ‘ร o tแบกo chฦฐฦกng trรฌnh tร i nฤƒng. Danh sรกch mรดn hแปc cแป‘t lรตi do Khoa ฤ‘แป nghแป‹ trong ฤ‘แป รกn mแปŸ chฦฐฦกng trรฌnh tร i nฤƒng. Seminar ngoแบกi khรณa: ฤ‘ฦฐแปฃc tแป• chแปฉc ngoร i buแป•i hแปc theo thแปi khรณa biแปƒu chรญnh thแปฉc ฤ‘แปƒ tฤƒng cฦฐแปng kแปน nฤƒng cho sinh vi... ฤiแปu 20. Tแป• chแปฉc ฤ‘รกnh giรก mรดn hแปc 1. ฤiแปu kiแป‡n dแปฑ thi kแบฟt thรบc hแปc phแบงn Sinh viรชn ฤ‘ฦฐแปฃc dแปฑ thi kแบฟt thรบc hแปc phแบงn khi cรณ mแบทt tแปซ 80% trแปŸ lรชn sแป‘ buแป•i hแปc theo thแปi khรณa biแปƒu cแปงa hแปc phแบงn ฤ‘รณ, trแปซ nhแปฏng sinh viรชn ฤ‘ฦฐแปฃc Hiแป‡u trฦฐแปŸng cho phรฉp hแปc chฦฐฦกng trรฌnh song ngร nh. Giแบฃng viรชn cรณ thแปƒ ฤ‘แปƒ nghแป‹ P.ฤTฤH khรดng cho sinh viรชn dแปฑ thi kแบฟt thรบc hแปc phแบงn theo quy ฤ‘แป‹nh riรชng cแปงa mรดn hแปc ฤ‘รฃ cรดng bแป‘ cho sinh viรชn. 2. Tแป• chแปฉc cรกc ฤ‘แปฃt thi tแบญp trung Mแป—i hแปc kแปณ chรญnh Trฦฐแปng tแป• chแปฉc mแป™t kแปณ kiแปƒm tra giแปฏa kแปณ vร  mแป™t kแปณ thi kแบฟt thรบc hแปc kแปณ. Hai ฤ‘แปฃt thi ฤ‘ฦฐแปฃc tแป• chแปฉc tแบญp trung theo kแบฟ hoแบกch giแบฃng dแบกy vร  hแปc tแบญp cแปงa Trฦฐแปng. Khoa, bแป™ mรดn, giแบฃng viรชn khรดng tแป• chแปฉc thi ngoร i cรกc ฤ‘แปฃt ฤ‘รฃ thรดng bรกo. Hiแป‡u trฦฐแปŸng quy ฤ‘แป‹nh thแปi gian hแปc, รดn thi vร  thi cho tแปซng hแปc kแปณ. 3. Tแป• chแปฉc ฤ‘รกnh giรก mรดn hแปc Viแป‡c tแป• chแปฉc ฤ‘รกnh giรก hแปc phแบงn ฤ‘ฦฐแปฃc thแปฑc hiแป‡n theo ฤ‘แป cฦฐฦกng chi tiแบฟt ฤ‘รฃ phรช duyแป‡t. Quy ฤ‘แป‹nh cแปฅ thแปƒ viแป‡c tแป• chแปฉc thi kแบฟt thรบc hแปc phแบงn, ra ฤ‘แป thi, chแบฅm thi, chแบฅm phรบc khแบฃo vร  xแปญ lรฝ ฤ‘iแปƒm sau chแบฅm phรบc khแบฃo; thรดng bรกo kแบฟt quแบฃ, cรดng tรกc b...
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
        "scale": 10.0,
        "num_negatives": 1,
        "activation_fn": "torch.nn.modules.activation.Sigmoid"
    }
    

Evaluation Dataset

  • model_id: KhoaUIT/KhoaUIT-CrossEncoder-UIT-R2GQA
  • evaluator: CrossEncoderRerankingEvaluator
  • valid set:
    map mrr@10 ndcg@10
    0.9969 0.9969 0.9977
  • test set:
    map mrr@10 ndcg@10
    0.9969 0.9969 0.9977

Unnamed Dataset

Customize original dataset UIT-R2GQA using function mine_hard_negatives

  • Size: 974 evaluation samples
  • Columns: question, context, and negative
  • Approximate statistics based on the first 974 samples:
    question context negative
    type string string string
    details
    • min: 11 characters
    • mean: 76.63 characters
    • max: 250 characters
    • min: 99 characters
    • mean: 1663.22 characters
    • max: 5480 characters
    • min: 99 characters
    • mean: 1323.13 characters
    • max: 5480 characters
  • Samples:
    question context negative
    Trฦฐแปng hแปฃp mแป™t SV ฤ‘ฦฐแปฃc CBCT cho xem bร i cแปงa SV khรกc trong giแป thi ฤ‘ฦฐแปฃc giแบฃi quyแบฟt nhฦฐ thแบฟ nร o? ฤiแปu 22. Xแปญ lรฝ cรกn bแป™ vi phแบกm quy ฤ‘แป‹nh Trong thแปi gian thi, nแบฟu cรกc cรกn bแป™ thanh tra thi phรกt hiแป‡n thแบฅy cรกc trฦฐแปng hแปฃp vi phแบกm thรฌ lแบญp biรชn bแบฃn tแบกi chแป— vร  ฤ‘แป nghแป‹ Hiแป‡u trฦฐแปŸng xแปญ lรฝ. Ngฦฐแปi tham gia cรดng tรกc thi cรณ hร nh vi vi phแบกm quy ฤ‘แป‹nh (bแป‹ phรกt hiแป‡n trong khi lร m nhiแป‡m vแปฅ hoแบทc sau kแปณ thi), nแบฟu cรณ ฤ‘แปง chแปฉng cแปฉ, tรนy theo mแปฉc ฤ‘แป™, sแบฝ bแป‹ xแปญ lรฝ theo cรกc hรฌnh thแปฉc sau ฤ‘รขy. 1. Khiแปƒn trรกch ฤ‘แป‘i vแป›i nhแปฏng ngฦฐแปi phแบกm lแป—i nhแบน trong khi thi hร nh nhiแป‡m vแปฅ. 2. Cแบฃnh cรกo ฤ‘แป‘i vแป›i nhแปฏng ngฦฐแปi vi phแบกm mแป™t trong cรกc lแป—i sau ฤ‘รขy: a. CBCT bแป coi thi mร  khรดng thรดng bรกo cho ฤ‘ฦกn vแป‹ tแป• chแปฉc mรดn thi tแปซ 2 lแบงn trแปŸ lรชn trong mแป™t ฤ‘แปฃt thi. b. CBCT ฤ‘แปƒ cho SV tแปฑ do trao ฤ‘แป•i khi lร m bร i, sแปญ dแปฅng tร i liแป‡u bแป‹ cแบฅm sแปญ dแปฅng hoแบทc sแปญ dแปฅng cรกc phฦฐฦกng tiแป‡n thรดng tin liรชn lแบกc tแบกi phรฒng thi. c. Cรกn bแป™ chแบฅm thi chแบฅm ฤ‘iแปƒm hoแบทc cแป™ng ฤ‘iแปƒm bร i thi cรณ nhiแปu sai sรณt (trรชn 5% tแป•ng sแป‘ bร i thi). d. Cรกn bแป™ chแบฅm thi nแป™p ฤ‘iแปƒm trแป… so vแป›i thแปi hแบกn quy ฤ‘แป‹nh. 3. Tรนy theo mแปฉc ฤ‘แป™ vi phแบกm cรณ thแปƒ bแป‹ hแบก bแบญc lฦฐฦกng, hแบก ngแบกch, cรกch chแปฉc hoแบทc chuyแปƒn ฤ‘i lร m ... ฤiแปu 10. Giแบฃng dแบกy cรกc mรดn CTTN CTTN phแบฃi ฤ‘ฦฐแปฃc thแปฑc hiแป‡n trรชn quan ฤ‘iแปƒm lแบฅy ngฦฐแปi hแปc lร m trung tรขm. Ngฦฐแปi hแปc phแบฃi ฤ‘ฦฐแปฃc tแบกo ฤ‘iแปu kiแป‡n ฤ‘แปƒ thแปƒ hiแป‡n vai trรฒ chแปง ฤ‘แป™ng trong tiแบฟn trรฌnh hแปc tแบญp. Ngฦฐแปi hแปc phแบฃi ฤ‘รณng vai trรฒ chแปง ฤ‘แป™ng trong hoแบกt ฤ‘แป™ng hแปc tแบญp, thay vรฌ thแปฅ ฤ‘แป™ng tiแบฟp nhแบญn kiแบฟn thแปฉc. Sinh viรชn CTTN sแบฝ hแปc cรนng vแป›i sinh viรชn cรกc lแป›p chฦฐฦกng trรฌnh chuแบฉn trong cรกc mรดn ฤ‘ฦฐแปฃc ฤ‘ร o tแบกo chung, cรกc mรดn hแปc cแป‘t lรตi dร nh riรชng cho sinh viรชn CTTN ฤ‘ฦฐแปฃc tแป• chแปฉc lแป›p hแปc riรชng. Khoa quแบฃn lรฝ chuyรชn mรดn cรณ trรกch nhiแป‡m chแปn cรกc cรกn bแป™ cรณ kinh nghiแป‡m ฤ‘แปƒ phแปฅ trรกch giแบฃng dแบกy. Cรกc mรดn hแปc tร i nฤƒng vร  KLTN phแบฃi do CBGD cรณ hแปc vแป‹ tiแบฟn sฤฉ hoแบทc giแบฃng viรชn chรญnh, hoแบทc thแบกc sฤฉ tแป‘t nghiแป‡p แปŸ cรกc trฦฐแปng ฤแบกi hแปc thuแป™c cรกc nฦฐแป›c tiรชn tiแบฟn, ฤ‘รบng ngร nh hoแบทc thuแป™c ngร nh gแบงn ฤ‘แบฃm nhiแป‡m. Trong tuแบงn ฤ‘แบงu tiรชn cแปงa hแปc kแปณ, CBGD phแบฃi thรดng bรกo cรดng khai cho sinh viรชn vแป ฤ‘แป cฦฐฦกng giแบฃng dแบกy mรดn hแปc; trong ฤ‘รณ ฤ‘แบทc biแป‡t chรบ รฝ cรกc thรดng tin, cรกc phแบงn hแปc bแป• sung tฤƒng cฦฐแปng; sแป‘ cแป™t ฤ‘iแปƒm vร  tแปท lแป‡ tรญnh cแปงa tแปซng cแป™t ฤ‘iแปƒm vร o ฤ‘iแปƒm tแป•ng kแบฟ...
    Cรณ thแปƒ nhแบญn chแปฉng chแป‰ hai lแบงn trแปŸ lรชn khรดng? ฤiแปu 2. Nguyรชn tแบฏc quแบฃn lรฝ, cแบฅp phรกt vฤƒn bแบฑng, chแปฉng chแป‰ 1. Vฤƒn bแบฑng, chแปฉng chแป‰ cแปงa Trฦฐแปng ฤHCNTT phรกt hร nh thuแป™c hแป‡ thแป‘ng vฤƒn bแบฑng, chแปฉng chแป‰ giรกo dแปฅc quแป‘c dรขn. 2. Vฤƒn bแบฑng, chแปฉng chแป‰ ฤ‘ฦฐแปฃc quแบฃn lรฝ thแป‘ng nhแบฅt, thแปฑc hiแป‡n phรขn cแบฅp quแบฃn lรฝ bแบฃo ฤ‘แบฃm quyแปn vร  trรกch nhiแป‡m cแปงa cรกc ฤ‘ฦกn vแป‹ trong Trฦฐแปng. 3. Vฤƒn bแบฑng, chแปฉng chแป‰ ฤ‘ฦฐแปฃc cแบฅp mแป™t lแบงn trแปซ trฦฐแปng hแปฃp quy ฤ‘แป‹nh tแบกi ฤiแปu 15 cแปงa Quy chแบฟ nร y. 4. Nghiรชm cแบฅm mแปi hร nh vi gian lแบญn trong cแบฅp phรกt vร  sแปญ dแปฅng vฤƒn bแบฑng, chแปฉng chแป‰. 5. Bแบฃo ฤ‘แบฃm cรดng khai, minh bแบกch trong cแบฅp phรกt vฤƒn bแบฑng, chแปฉng chแป‰. ฤiแปu 5. Xรฉt miแป…n cรกc mรดn hแปc tiแบฟng Anh 1. Sinh viรชn ฤ‘ฦฐแปฃc xรฉt miแป…n hแปc cรกc mรดn tiแบฟng Anh tฦฐฦกng แปฉng nแบฟu cรณ mแป™t trong cรกc chแปฉng chแป‰/chแปฉng nhแบญn ฤ‘แบกt trรฌnh ฤ‘แป™ tแป‘i thiแปƒu theo quy ฤ‘แป‹nh tแบกi Bแบฃng 3. Sinh viรชn ฤ‘ฦฐแปฃc miแป…n mรดn hแปc nร o thรฌ ฤ‘แป“ng thแปi ฤ‘ฦฐแปฃc miแป…n nhแปฏng mรดn hแปc แปŸ cแบฅp ฤ‘แป™ thแบฅp hฦกn mร  sinh viรชn chฦฐa hแปc hoแบทc chฦฐa ฤ‘แบกt. Dฦฐแป›i ฤ‘รขy lร  cรกc mแปฉc ฤ‘iแปƒm cแปงa chแปฉng chแป‰/chแปฉng nhแบญn vร  mรดn hแปc ฤ‘ฦฐแปฃc miแป…n theo cรกc ฤ‘iแปƒm sแป‘ tฦฐฦกng แปฉng: TOEIC Nghe-ฤแปc ฤ‘แบกt tแปซ 350 - dฦฐแป›i 400, TOEIC Nรณi-Viแบฟt ฤ‘แบกt tแปซ 100 โ€“ dฦฐแป›i 120, TOEFL iBT ฤ‘แบกt tแปซ 32 โ€“ dฦฐแป›i 35, IELTS ฤ‘แบกt 3.5, VNU-EPT ฤ‘แบกt tแปซ 152 - dฦฐแป›i 165, chแปฉng chแป‰ Cambridge English KET (Merit) hay A2 Key (Grade C) sแบฝ ฤ‘ฦฐแปฃc miแป…n Anh vฤƒn 1 (mรฃ mรดn ENG01) ฤ‘แป‘i vแป›i cแบฃ chฦฐฦกng trรฌnh CTC/ CTTN vร  CTTT. TOEIC Nghe-ฤแปc ฤ‘แบกt tแปซ 400 - dฦฐแป›i 450, TOEIC Nรณi-Viแบฟt ฤ‘แบกt tแปซ 120 โ€“ dฦฐแป›i 140, TOEFL iBT ฤ‘แบกt tแปซ 32 โ€“ dฦฐแป›i 35, IELTS ฤ‘แบกt 3.5, VNU-EPT ฤ‘แบกt tแปซ 152 - dฦฐแป›i 165, chแปฉng chแป‰ Cambridge English KET (Merit) hay A2 Key (Grade C) sแบฝ ฤ‘ฦฐแปฃc miแป…n Anh vฤƒn 2 (mรฃ mรดn ENG02) ฤ‘แป‘i vแป›i cแบฃ chฦฐฦกng trรฌnh CTC/ CTTN vร  CTT...
    ฤฦกn vแป‹ nร o chแป‹u trรกch nhiแป‡m xแบฟp lแป‹ch thi, ฤ‘ฦฐa thรดng tin lแป‹ch thi cรกc ฤ‘แปฃt thi tแบญp trung hแป‡ ฤ‘แบกi hแปc chรญnh quy lรชn Hแป‡ thแป‘ng ? V. Nแป˜I DUNG QUY TRรŒNH Quy trรฌnh phรขn cรดng cรกn bแป™ coi thi gแป“m 3 giai ฤ‘oแบกn tฦฐฦกng แปฉng vแป›i 3 quy trรฌnh thร nh phแบงn: ฤฤƒng kรฝ coi thi, Phรขn cรดng cรกn bแป™ coi thi vร  Tแป•ng kแบฟt coi thi. Chi tiแบฟt cรกc quy trรฌnh nhฦฐ sau. 1. Quy trรฌnh ฤ‘ฤƒng kรฝ coi thi Bฦฐแป›c 1: Xรขy dแปฑng kแบฟ hoแบกch thi Phรฒng ฤร o tแบกo ฤแบกi hแปc xรขy dแปฑng vร  thรดng bรกo kแบฟ hoแบกch thi cho cรกc ฤ‘ฦกn vแป‹ tแป• chแปฉc thi; trong ฤ‘รณ xรกc ฤ‘แป‹nh rรต cรกc mแป‘c thแปi gian chรญnh. Thแปi gian trฦฐแป›c 21 ngร y tรญnh tแปซ ngร y mแปŸ hแป‡ thแป‘ng ฤ‘ฤƒng kรฝ coi thi. Bฦฐแป›c 2: Xแบฟp lแป‹ch thi, ฤ‘ฦฐa lแป‹ch thi lรชn hแป‡ thแป‘ng Phรฒng ฤร o tแบกo ฤแบกi hแปc xแบฟp lแป‹ch thi, ฤ‘ฦฐa thรดng tin lแป‹ch thi lรชn Hแป‡ thแป‘ng vร  gแปญi cho cรกc ฤ‘ฦกn vแป‹ tแป• chแปฉc thi ฤ‘แปƒ chuแบฉn bแป‹ cho cรดng tรกc ฤ‘ฤƒng kรฝ coi thi. Hแป‡ thแป‘ng hแป— trแปฃ kiแปƒm tra tรญnh hแปฃp lแป‡ cแปงa lแป‹ch thi. Thแปi gian trฦฐแป›c 21 ngร y kแปƒ tแปซ ngร y mแปŸ hแป‡ thแป‘ng ฤ‘ฤƒng kรฝ coi thi. Bฦฐแป›c 3: Thรดng bรกo cรกn bแป™ vร  sinh viรชn ฤ‘ฤƒng kรฝ coi thi Phรฒng ฤร o tแบกo ฤแบกi hแปc thรดng bรกo cho cรกn bแป™ vร  sinh viรชn ฤ‘ฤƒng kรฝ coi thi qua email hoแบทc trรชn trang web Cแป•ng thรดng tin ฤ‘ร o tแบกo ฤ‘แบกi hแปc. Thแปi gian trฦฐแป›c 7 ngร y kแปƒ tแปซ ngร y mแปŸ hแป‡... ฤiแปu 9. Nhรขn ฤ‘แป thi vร  ฤ‘รณng gรณi ฤ‘แป thi 1. ฤฦกn vแป‹ tแป• chแปฉc thi cรณ trรกch nhiแป‡m nhรขn ฤ‘แป thi vร  ฤ‘รณng gรณi thร nh cรกc tรบi ฤ‘แป thi cho nhแปฏng mรดn thi mร  mรฌnh phแปฅ trรกch. Mแป—i ฤ‘แปฃt thi, ฤ‘ฦกn vแป‹ tแป• chแปฉc thi cแปญ 01 cรกn bแป™ phแปฅ trรกch chรญnh cรดng tรกc nhรขn ฤ‘แป thi cho kแปณ thi. ฤฦกn vแป‹ tแป• chแปฉc thi chuแบฉn bแป‹ nhรฃn tรบi ฤ‘แปฑng ฤ‘แป thi vร  liรชn hแป‡ Thฦฐ viแป‡n ฤ‘ฤƒng kรฝ lแป‹ch nhรขn ฤ‘แป thi. 2. Thฦฐ viแป‡n chแป‹u trรกch nhiแป‡m cแปญ cรกn bแป™ hแป— trแปฃ kแปน thuแบญt trong quรก trรฌnh nhรขn ฤ‘แป thi tแบกi Thฦฐ viแป‡n khi ฤฦกn vแป‹ tแป• chแปฉc thi yรชu cแบงu. 3. Trฦฐแปng hแปฃp tแป• chแปฉc thi trรชn mรกy tรญnh tแบกi phรฒng mรกy thรฌ Phรฒng Dแปฏ liแป‡u vร  Cรดng nghแป‡ Thรดng tin cแปญ cรกn bแป™ hแป— trแปฃ kแปน thuแบญt trong quรก trรฌnh chuแบฉn bแป‹ phรฒng mรกy; quรก trรฌnh nhแบญp ฤ‘แป thi, trแป™n ฤ‘แป thi; vร  hแป— trแปฃ trong quรก trรฌnh thi tแบกi phรฒng mรกy. 4. Trong quรก trรฌnh nhรขn ฤ‘แป thi, cรกn bแป™ phแปฅ trรกch nhรขn ฤ‘แป thi phแบฃi kiแปƒm tra chแบฅt lฦฐแปฃng bแบฃn in. Cรกc bแบฃn in thแปญ phแบฃi ฤ‘ฦฐแปฃc thu lแบกi vร  bแบฃo quแบฃn theo chแบฟ ฤ‘แป™ tร i liแป‡u mแบญt. 5. Khi ฤ‘รณng gรณi ฤ‘แป thi phแบฃi bแบฃo ฤ‘แบฃm ฤ‘แป thi ฤ‘รบng mรดn thi ghi แปŸ phong bรฌ ฤ‘แป thi, ฤ‘แปง sแป‘ lฦฐแปฃng ฤ‘แป thi cho tแปซng phรฒng thi vร  dฦฐ...
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
        "scale": 10.0,
        "num_negatives": 1,
        "activation_fn": "torch.nn.modules.activation.Sigmoid"
    }
    

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • learning_rate: 1e-05
  • warmup_ratio: 0.1
  • fp16: True
  • dataloader_drop_last: True
  • load_best_model_at_end: True
  • batch_sampler: no_duplicates

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: steps
  • prediction_loss_only: True
  • per_device_train_batch_size: 8
  • per_device_eval_batch_size: 8
  • per_gpu_train_batch_size: None
  • per_gpu_eval_batch_size: None
  • gradient_accumulation_steps: 1
  • eval_accumulation_steps: None
  • torch_empty_cache_steps: None
  • learning_rate: 1e-05
  • weight_decay: 0.0
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1.0
  • num_train_epochs: 3
  • max_steps: -1
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.1
  • warmup_steps: 0
  • log_level: passive
  • log_level_replica: warning
  • log_on_each_node: True
  • logging_nan_inf_filter: True
  • save_safetensors: True
  • save_on_each_node: False
  • save_only_model: False
  • restore_callback_states_from_checkpoint: False
  • no_cuda: False
  • use_cpu: False
  • use_mps_device: False
  • seed: 42
  • data_seed: None
  • jit_mode_eval: False
  • use_ipex: False
  • bf16: False
  • fp16: True
  • fp16_opt_level: O1
  • half_precision_backend: auto
  • bf16_full_eval: False
  • fp16_full_eval: False
  • tf32: None
  • local_rank: 0
  • ddp_backend: None
  • tpu_num_cores: None
  • tpu_metrics_debug: False
  • debug: []
  • dataloader_drop_last: True
  • dataloader_num_workers: 0
  • dataloader_prefetch_factor: None
  • past_index: -1
  • disable_tqdm: False
  • remove_unused_columns: True
  • label_names: None
  • load_best_model_at_end: True
  • ignore_data_skip: False
  • fsdp: []
  • fsdp_min_num_params: 0
  • fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
  • fsdp_transformer_layer_cls_to_wrap: None
  • accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
  • deepspeed: None
  • label_smoothing_factor: 0.0
  • optim: adamw_torch
  • optim_args: None
  • adafactor: False
  • group_by_length: False
  • length_column_name: length
  • ddp_find_unused_parameters: None
  • ddp_bucket_cap_mb: None
  • ddp_broadcast_buffers: False
  • dataloader_pin_memory: True
  • dataloader_persistent_workers: False
  • skip_memory_metrics: True
  • use_legacy_prediction_loop: False
  • push_to_hub: False
  • resume_from_checkpoint: None
  • hub_model_id: None
  • hub_strategy: every_save
  • hub_private_repo: None
  • hub_always_push: False
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • include_inputs_for_metrics: False
  • include_for_metrics: []
  • eval_do_concat_batches: True
  • fp16_backend: auto
  • push_to_hub_model_id: None
  • push_to_hub_organization: None
  • mp_parameters:
  • auto_find_batch_size: False
  • full_determinism: False
  • torchdynamo: None
  • ray_scope: last
  • ddp_timeout: 1800
  • torch_compile: False
  • torch_compile_backend: None
  • torch_compile_mode: None
  • include_tokens_per_second: False
  • include_num_input_tokens_seen: False
  • neftune_noise_alpha: None
  • optim_target_modules: None
  • batch_eval_metrics: False
  • eval_on_start: False
  • use_liger_kernel: False
  • eval_use_gather_object: False
  • average_tokens_across_devices: False
  • prompts: None
  • batch_sampler: no_duplicates
  • multi_dataset_batch_sampler: proportional

Training Logs

Epoch Step Training Loss Validation Loss
0.2564 250 0.9351 0.1729
0.5128 500 0.1219 0.0746
0.7692 750 0.0909 0.0677
1.0256 1000 0.0558 0.0603
1.2821 1250 0.0452 0.0428
1.5385 1500 0.0364 0.0432
1.7949 1750 0.0468 0.0334

Framework Versions

  • Python: 3.11.11
  • Sentence Transformers: 4.1.0
  • Transformers: 4.52.4
  • PyTorch: 2.6.0+cu124
  • Accelerate: 1.5.2
  • Datasets: 3.6.0
  • Tokenizers: 0.21.1

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
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