CrossEncoder based on AITeamVN/Vietnamese_Reranker

This is a Cross Encoder model finetuned from AITeamVN/Vietnamese_Reranker 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: AITeamVN/Vietnamese_Reranker
  • Maximum Sequence Length: 512 tokens
  • Number of Output Labels: 1 label
  • Supported Modality: Text

Model Sources

Full Model Architecture

CrossEncoder(
  (0): Transformer({'transformer_task': 'sequence-classification', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}}, 'module_output_name': 'scores', 'architecture': 'XLMRobertaForSequenceClassification'})
)

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("cross_encoder_model_id")
# Get scores for pairs of inputs
pairs = [
    ['Nhรขn viรชn phแปฅc vแปฅ xe khรกch khรดng hแป— trแปฃ, giรบp ฤ‘แปก hร nh khรกch lร  ngฦฐแปi cao tuแป•i, trแบป em bแป‹ phแบกt bao nhiรชu?', 'ฤiแปu 59 - Luแบญt ฤฦฐแปng bแป™ : Quyแปn vร  nghฤฉa vแปฅ cแปงa ngฦฐแปi lรกi xe, nhรขn viรชn phแปฅc vแปฅ trรชn xe รด tรด kinh doanh vแบญn tแบฃi hร nh khรกch\n1. Tแปซ chแป‘i vแบญn tแบฃi hร nh khรกch cรณ mแป™t trong cรกc hร nh vi sau ฤ‘รขy:\na) Gรขy rแป‘i trแบญt tแปฑ cรดng cแป™ng trรชn xe รด tรด;\nb) Cแบฃn trแปŸ cรดng viแป‡c cแปงa ngฦฐแปi lรกi xe, nhรขn viรชn phแปฅc vแปฅ trรชn xe รด tรด;\nc) Gรขy แบฃnh hฦฐแปŸng ฤ‘แบฟn tรญnh mแบกng, sแปฉc khoแบป, tร i sแบฃn cแปงa ngฦฐแปi khรกc trรชn xe รด tรด;\nd) Gian lแบญn vรฉ;\nฤ‘) Vi phแบกm quy ฤ‘แป‹nh tแบกi ฤ‘iแปƒm c khoแบฃn 2 ฤiแปu 60 cแปงa Luแบญt nร y.\n2. Tแปซ chแป‘i ฤ‘iแปu khiแปƒn phฦฐฦกng tiแป‡n khi phรกt hiแป‡n phฦฐฦกng tiแป‡n khรดng bแบฃo ฤ‘แบฃm cรกc ฤ‘iแปu kiแป‡n vแป an toร n theo quy ฤ‘แป‹nh cแปงa phรกp luแบญt vแป trแบญt tแปฑ, an toร n giao thรดng ฤ‘ฦฐแปng bแป™.\n3. Khรดng ฤ‘ฦฐแปฃc tแปซ chแป‘i vแบญn tแบฃi hร nh khรกch, trแปซ lรฝ do quy ฤ‘แป‹nh tแบกi khoแบฃn 1 ฤiแปu nร y; khรดng ฤ‘ฦฐแปฃc gรขy khรณ khฤƒn ฤ‘แป‘i vแป›i hร nh khรกch lร  ngฦฐแปi cao tuแป•i, ngฦฐแปi khuyแบฟt tแบญt, trแบป em, phแปฅ nแปฏ mang thai; khรดng ฤ‘ฦฐแปฃc chuyแปƒn hร nh khรกch sang phฦฐฦกng tiแป‡n khรกc khi chฦฐa ฤ‘ฦฐแปฃc sแปฑ ฤ‘แป“ng รฝ cแปงa hร nh khรกch, trแปซ trฦฐแปng hแปฃp bแบฅt khแบฃ khรกng.\n4. Thu tiแปn ฤ‘รบng giรก vรฉ niรชm yแบฟt hoแบทc theo hแปฃp ฤ‘แป“ng vแบญn tแบฃi.\n5. Cรณ thรกi ฤ‘แป™, lแปi nรณi, hร nh vi vฤƒn minh, lแป‹ch sแปฑ; hฦฐแป›ng dแบซn hร nh khรกch ngแป“i ฤ‘รบng nฦกi quy ฤ‘แป‹nh, bแป‘ trรญ chแป— ngแป“i ฦฐu tiรชn cho ngฦฐแปi cao tuแป•i, ngฦฐแปi khuyแบฟt tแบญt, trแบป em vร  phแปฅ nแปฏ mang thai.\n6. Thแปฑc hiแป‡n trรกch nhiแป‡m theo quy ฤ‘แป‹nh cแปงa phรกp luแบญt vแป trแบญt tแปฑ, an toร n giao thรดng ฤ‘ฦฐแปng bแป™ vร  quy ฤ‘แป‹nh khรกc cแปงa phรกp luแบญt cรณ liรชn quan.'],
    ['Trรฌnh tแปฑ vร  nแป™i dung lร m viแป‡c cแปงa hแป™i ฤ‘แป“ng tฦฐ vแบฅn tuyแปƒn chแปn, giao trแปฑc tiแบฟp tแป• chแปฉc chแปง trรฌ dแปฑ รกn Chฦฐฦกng trรฌnh phรกt triแปƒn tร i sแบฃn trรญ tuแป‡ ฤ‘ฦฐแปฃc quy ฤ‘แป‹nh nhฦฐ thแบฟ nร o?', 'ฤiแปu 18. Lแบฅy รฝ kiแบฟn tฦฐ vแบฅn cแปงa chuyรชn gia ฤ‘แป™c lแบญp vแป viแป‡c tuyแปƒn chแปn, giao trแปฑc tiแบฟp tแป• chแปฉc, cรก nhรขn chแปง trรฌ nhiแป‡m vแปฅ cแปงa Chฦฐฦกng trรฌnh : 1. ฤฦกn vแป‹ quแบฃn lรฝ nhiแป‡m vแปฅ cแปงa Chฦฐฦกng trรฌnh trรฌnh lรฃnh ฤ‘แบกo Bแป™ quyแบฟt ฤ‘แป‹nh lแปฑa chแปn 03 chuyรชn gia ฤ‘แป™c lแบญp ฤ‘แปƒ lแบฅy รฝ kiแบฟn tฦฐ vแบฅn vแป viแป‡c tuyแปƒn chแปn tแป• chแปฉc, tแป• chแปฉc chแปง trรฌ nhiแป‡m vแปฅ cแปงa Chฦฐฦกng trรฌnh.\n2. Chuyรชn gia ฤ‘แป™c lแบญp cรณ รฝ kiแบฟn tฦฐ vแบฅn bแบฑng vฤƒn bแบฃn sau khi ฤ‘รกnh giรก cรกc hแป“ sฦก tham gia tuyแปƒn chแปn, giao trแปฑc tiแบฟp tแป• chแปฉc, tแป• chแปฉc chแปง trรฌ nhiแป‡m vแปฅ. Chuyรชn gia ฤ‘แป™c lแบญp lร m viแป‡c ฤ‘แป™c lแบญp vร  chแป‹u trรกch nhiแป‡m trฦฐแป›c phรกp luแบญt vแป nhแบญn xรฉt, ฤ‘รกnh giรก vร  kแบฟt luแบญn cแปงa mรฌnh.\n3. ฤฦกn vแป‹ quแบฃn lรฝ nhiแป‡m vแปฅ cแปงa Chฦฐฦกng trรฌnh tแป• chแปฉc giao hแป“ sฦก cho cรกc chuyรชn gia ฤ‘แป™c lแบญp ฤ‘แปƒ thแปฑc hiแป‡n viแป‡c ฤ‘รกnh giรก lแบกi hแป“ sฦก cรกc nhiแป‡m vแปฅ tham gia tuyแปƒn chแปn, giao trแปฑc tiแบฟp. Thแปi gian ฤ‘รกnh giรก hแป“ sฦก tแป‘i ฤ‘a 15 ngร y kแปƒ tแปซ khi nhแบญn hแป“ sฦก.\n4. Hแป“ sฦก tuyแปƒn chแปn, giao trแปฑc tiแบฟp ฤ‘ฦฐแปฃc xem xรฉt phรช duyแป‡t nแบฟu cรณ รญt nhแบฅt 02 chuyรชn gia ฤ‘แป™c lแบญp ฤ‘รกnh giรก 70 ฤ‘iแปƒm trแปŸ lรชn.'],
    ['Cรกc bฦฐแป›c thแปฑc hiแป‡n viแป‡c mแปŸ vร  ฤ‘รกnh giรก hแป“ sฦก ฤ‘แป xuแบฅt vแป tร i chรญnh - thฦฐฦกng mแบกi cแปงa nhร  ฤ‘แบงu tฦฐ dแปฑ รกn ฤ‘แบงu tฦฐ cรณ sแปญ dแปฅng ฤ‘แบฅt ฤ‘ฦฐแปฃc quy ฤ‘แป‹nh nhฦฐ thแบฟ nร o?', 'ฤiแปu 27. Quy trรฌnh chi tiแบฟt : 1. Chuแบฉn bแป‹ lแปฑa chแปn nhร  ฤ‘แบงu tฦฐ, bao gแป“m:\na) Lแบญp hแป“ sฦก mแปi thแบงu;\nb) Thแบฉm ฤ‘แป‹nh vร  phรช duyแป‡t hแป“ sฦก mแปi thแบงu.\n2. Tแป• chแปฉc lแปฑa chแปn nhร  ฤ‘แบงu tฦฐ, bao gแป“m:\na) Mแปi thแบงu;\nb) Phรกt hร nh, sแปญa ฤ‘แป•i, lร m rรต hแป“ sฦก mแปi thแบงu;\nc) Chuแบฉn bแป‹, nแป™p, tiแบฟp nhแบญn, quแบฃn lรฝ, sแปญa ฤ‘แป•i, rรบt hแป“ sฦก dแปฑ thแบงu;\nd) MแปŸ hแป“ sฦก ฤ‘แป xuแบฅt vแป kแปน thuแบญt.\n3. ฤรกnh giรก hแป“ sฦก ฤ‘แป xuแบฅt vแป kแปน thuแบญt, bao gแป“m:\na) Kiแปƒm tra, ฤ‘รกnh giรก tรญnh hแปฃp lแป‡ cแปงa hแป“ sฦก ฤ‘แป xuแบฅt vแป kแปน thuแบญt;\nb) ฤรกnh giรก chi tiแบฟt hแป“ sฦก ฤ‘แป xuแบฅt vแป kแปน thuแบญt;\nc) Thแบฉm ฤ‘แป‹nh, phรช duyแป‡t danh sรกch nhร  ฤ‘แบงu tฦฐ ฤ‘รกp แปฉng yรชu cแบงu vแป kแปน thuแบญt.\n4. MแปŸ vร  ฤ‘รกnh giรก hแป“ sฦก ฤ‘แป xuแบฅt vแป tร i chรญnh - thฦฐฦกng mแบกi, bao gแป“m:\na) MแปŸ hแป“ sฦก ฤ‘แป xuแบฅt vแป tร i chรญnh - thฦฐฦกng mแบกi;\nb) Kiแปƒm tra, ฤ‘รกnh giรก tรญnh hแปฃp lแป‡ cแปงa hแป“ sฦก ฤ‘แป xuแบฅt vแป tร i chรญnh - thฦฐฦกng mแบกi;\nc) ฤรกnh giรก chi tiแบฟt hแป“ sฦก ฤ‘แป xuแบฅt vแป tร i chรญnh - thฦฐฦกng mแบกi vร  xแบฟp hแบกng nhร  ฤ‘แบงu tฦฐ.\n5. Trรฌnh, thแบฉm ฤ‘แป‹nh, phรช duyแป‡t vร  cรดng khai kแบฟt quแบฃ lแปฑa chแปn nhร  ฤ‘แบงu tฦฐ.\n6. ฤร m phรกn, hoร n thiแป‡n, kรฝ kแบฟt hแปฃp ฤ‘แป“ng, bao gแป“m:\na) ฤร m phรกn, hoร n thiแป‡n hแปฃp ฤ‘แป“ng;\nb) Kรฝ kแบฟt hแปฃp ฤ‘แป“ng vร  cรดng khai thรดng tin hแปฃp ฤ‘แป“ng dแปฑ รกn.\n7. Trฦฐแปng hแปฃp dแปฑ รกn แปฉng dแปฅng cรดng nghแป‡ cao, quy trรฌnh lแปฑa chแปn nhร  ฤ‘แบงu tฦฐ thแปฑc hiแป‡n theo quy ฤ‘แป‹nh tแบกi cรกc khoแบฃn 1, 2, 3, 4 vร  5 ฤiแปu nร y. Nhร  ฤ‘แบงu tฦฐ ฤ‘ฦฐแปฃc lแปฑa chแปn sแบฝ tiแบฟn hร nh lแบญp bรกo cรกo nghiรชn cแปฉu khแบฃ thi. Trรชn cฦก sแปŸ bรกo cรกo nghiรชn cแปฉu khแบฃ thi ฤ‘ฦฐแปฃc duyแป‡t, cฦก quan nhร  nฦฐแป›c cรณ thแบฉm quyแปn vร  nhร  ฤ‘แบงu tฦฐ tiแบฟn hร nh ฤ‘ร m phรกn, hoร n thiแป‡n vร  kรฝ kแบฟt hแปฃp ฤ‘แป“ng. Nแป™i dung bรกo cรกo nghiรชn cแปฉu khแบฃ thi vร  hแปฃp ฤ‘แป“ng phแบฃi bแบฃo ฤ‘แบฃm phรน hแปฃp vแป›i mแปฅc tiรชu thแปฑc hiแป‡n dแปฑ รกn, quy mรด ฤ‘แบงu tฦฐ, cรกc tiรชu chรญ cฦก bแบฃn ฤ‘แปƒ xรฉt duyแป‡t nhร  ฤ‘แบงu tฦฐ trรบng thแบงu.'],
    ['Trฦฐแปng chuyรชn ฤ‘ฦฐแปฃc thร nh lแบญp แปŸ cแบฅp trung hแปc phแป• thรดng dร nh cho hแปc sinh ฤ‘แบกt kแบฟt quแบฃ xuแบฅt sแบฏc trong hแปc tแบญp nhแบฑm phรกt triแปƒn nฤƒng khiแบฟu vแป mแป™t sแป‘ mรดn hแปc trรชn cฦก sแปŸ nร o sau ฤ‘รขy?', 'ฤiแปu 28 - Luแบญt Giรกo dแปฅc : Cแบฅp hแปc vร  ฤ‘แป™ tuแป•i cแปงa giรกo dแปฅc phแป• thรดng\n\n1. Cรกc cแบฅp hแปc vร  ฤ‘แป™ tuแป•i cแปงa giรกo dแปฅc phแป• thรดng ฤ‘ฦฐแปฃc quy ฤ‘แป‹nh nhฦฐ sau:\n\na) Giรกo dแปฅc tiแปƒu hแปc ฤ‘ฦฐแปฃc thแปฑc hiแป‡n trong 05 nฤƒm hแปc, tแปซ lแป›p mแป™t ฤ‘แบฟn hแบฟt lแป›p nฤƒm. Tuแป•i cแปงa hแปc sinh vร o hแปc lแป›p mแป™t lร  06 tuแป•i vร  ฤ‘ฦฐแปฃc tรญnh theo nฤƒm;\n\nb) Giรกo dแปฅc trung hแปc cฦก sแปŸ ฤ‘ฦฐแปฃc thแปฑc hiแป‡n trong 04 nฤƒm hแปc, tแปซ lแป›p sรกu ฤ‘แบฟn hแบฟt lแป›p chรญn. Hแปc sinh vร o hแปc lแป›p sรกu phแบฃi hoร n thร nh chฦฐฦกng trรฌnh tiแปƒu hแปc. Tuแป•i cแปงa hแปc sinh vร o hแปc lแป›p sรกu lร  11 tuแป•i vร  ฤ‘ฦฐแปฃc tรญnh theo nฤƒm;\n\nc) Giรกo dแปฅc trung hแปc phแป• thรดng ฤ‘ฦฐแปฃc thแปฑc hiแป‡n trong 03 nฤƒm hแปc, tแปซ lแป›p mฦฐแปi ฤ‘แบฟn hแบฟt lแป›p mฦฐแปi hai. Hแปc sinh vร o hแปc lแป›p mฦฐแปi phแบฃi cรณ bแบฑng tแป‘t nghiแป‡p trung hแปc cฦก sแปŸ. Tuแป•i cแปงa hแปc sinh vร o hแปc lแป›p mฦฐแปi lร  15 tuแป•i vร  ฤ‘ฦฐแปฃc tรญnh theo nฤƒm.\n\n2. Trฦฐแปng hแปฃp hแปc sinh ฤ‘ฦฐแปฃc hแปc vฦฐแปฃt lแป›p, hแปc แปŸ ฤ‘แป™ tuแป•i cao hฦกn tuแป•i quy ฤ‘แป‹nh tแบกi khoแบฃn 1 ฤiแปu nร y bao gแป“m:\n\na) Hแปc sinh hแปc vฦฐแปฃt lแป›p trong trฦฐแปng hแปฃp phรกt triแปƒn sแป›m vแป trรญ tuแป‡;\n\nb) Hแปc sinh hแปc แปŸ ฤ‘แป™ tuแป•i cao hฦกn tuแป•i quy ฤ‘แป‹nh trong trฦฐแปng hแปฃp hแปc sinh hแปc lฦฐu ban, hแปc sinh แปŸ vรนng cรณ ฤ‘iแปu kiแป‡n kinh tแบฟ - xรฃ hแป™i ฤ‘แบทc biแป‡t khรณ khฤƒn, hแปc sinh lร  ngฦฐแปi dรขn tแป™c thiแปƒu sแป‘, hแปc sinh lร  ngฦฐแปi khuyแบฟt tแบญt, hแปc sinh kรฉm phรกt triแปƒn vแป thแปƒ lแปฑc hoแบทc trรญ tuแป‡, hแปc sinh mแป“ cรดi khรดng nฦกi nฦฐฦกng tแปฑa, hแปc sinh thuแป™c hแป™ nghรจo, hแปc sinh แปŸ nฦฐแป›c ngoร i vแป nฦฐแป›c vร  trฦฐแปng hแปฃp khรกc theo quy ฤ‘แป‹nh cแปงa phรกp luแบญt.\n\n3. Giรกo dแปฅc phแป• thรดng ฤ‘ฦฐแปฃc chia thร nh giai ฤ‘oแบกn giรกo dแปฅc cฦก bแบฃn vร  giai ฤ‘oแบกn giรกo dแปฅc ฤ‘แป‹nh hฦฐแป›ng nghแป nghiแป‡p. Giai ฤ‘oแบกn giรกo dแปฅc cฦก bแบฃn gแป“m cแบฅp tiแปƒu hแปc vร  cแบฅp trung hแปc cฦก sแปŸ; giai ฤ‘oแบกn giรกo dแปฅc ฤ‘แป‹nh hฦฐแป›ng nghแป nghiแป‡p lร  cแบฅp trung hแปc phแป• thรดng. Hoฬฃc sinh trong cฦก sแปŸ giรกo dแปฅc nghรชฬ€ nghiรชฬฃp ฤ‘ฦฐฦกฬฃc hoฬฃc khแป‘i lฦฐแปฃng kiรชฬn thฦฐฬc vฤƒn hรณa trung hแปc phรดฬ‰ thรดng.\n\n4. Bแป™ trฦฐแปŸng Bแป™ Giรกo dแปฅc vร  ฤร o tแบกo quy ฤ‘แป‹nh viแป‡c dแบกy vร  hแปc tiแบฟng Viแป‡t cho trแบป em lร  ngฦฐแปi dรขn tแป™c thiแปƒu sแป‘ trฦฐแป›c khi vร o hแปc lแป›p mแป™t; viแป‡c giแบฃng dแบกy khแป‘i lฦฐแปฃng kiแบฟn thฦฐฬc vฤƒn hoฬa trung hoฬฃc phรดฬ‰ thรดng trong cฦก sแปŸ giรกo dแปฅc nghแป nghiแป‡p; cรกc trฦฐแปng hแปฃp quy ฤ‘แป‹nh tแบกi khoแบฃn 2 ฤiแปu nร y.'],
    ['Dแป‹ch vแปฅ viแป‡c lร m gแป“m cรณ nhแปฏng hoแบกt ฤ‘แป™ng nร o?', 'ฤiแปu 12. Hร nh vi vแบญn chuyแปƒn trรกi phรฉp hร ng hรณa, tiแปn tแป‡ qua biรชn giแป›i ngoร i khu vแปฑc cแปญa khแบฉu nhฦฐng chฦฐa ฤ‘แบฟn mแปฉc truy cแปฉu trรกch nhiแป‡m hรฌnh sแปฑ : 1. Phแบกt tiแปn tแปซ 1.000.000 ฤ‘แป“ng ฤ‘แบฟn 5.000.000 ฤ‘แป“ng ฤ‘แป‘i vแป›i hร nh vi vi phแบกm mร  tang vแบญt vi phแบกm cรณ giรก trแป‹ dฦฐแป›i 10.000.000 ฤ‘แป“ng.\n2. Phแบกt tiแปn tแปซ 5.000.000 ฤ‘แป“ng ฤ‘แบฟn 10.000.000 ฤ‘แป“ng ฤ‘แป‘i vแป›i mแป™t trong nhแปฏng hร nh vi sau:\na) Hร nh vi vi phแบกm mร  tang vแบญt vi phแบกm cรณ giรก trแป‹ tแปซ 10.000.000 ฤ‘แป“ng ฤ‘แบฟn dฦฐแป›i 50.000.000 ฤ‘แป“ng;\nb) Vแบญn chuyแปƒn trรกi phรฉp qua biรชn giแป›i hร ng hรณa lร  vฤƒn hรณa phแบฉm.\n3. Phแบกt tiแปn tแปซ 20.000.000 ฤ‘แป“ng ฤ‘แบฟn 40.000.000 ฤ‘แป“ng ฤ‘แป‘i vแป›i hร nh vi vi phแบกm mร  tang vแบญt vi phแบกm cรณ giรก trแป‹ tแปซ 50.000.000 ฤ‘แป“ng ฤ‘แบฟn dฦฐแป›i 100.000.000 ฤ‘แป“ng?\n4. Phแบกt tiแปn tแปซ 40.000.000 ฤ‘แป“ng ฤ‘แบฟn 50.000.000 ฤ‘แป“ng ฤ‘แป‘i vแป›i tang vแบญt vi phแบกm cรณ giรก trแป‹ tแปซ 100.000.000 ฤ‘แป“ng trแปŸ lรชn.\n5. Hรฌnh thแปฉc xแปญ phแบกt bแป• sung: Tแป‹ch thu tang vแบญt ฤ‘แป‘i vแป›i hร nh vi vi phแบกm quy ฤ‘แป‹nh tแบกi ฤiแปu nร y, trแปซ trฦฐแปng hแปฃp ฤ‘รฃ รกp dแปฅng biแป‡n phรกp khแบฏc phแปฅc hแบญu quแบฃ ฤ‘ฦฐแปฃc quy ฤ‘แป‹nh tแบกi khoแบฃn 6 ฤiแปu nร y.\n6. Biแป‡n phรกp khแบฏc phแปฅc hแบญu quแบฃ: Buแป™c tiรชu hแปงy hร ng hรณa, vแบญt phแบฉm gรขy hแบกi cho sแปฉc khแปe con ngฦฐแปi, vแบญt nuรดi, cรขy trแป“ng, mรดi trฦฐแปng vร  vฤƒn hรณa phแบฉm cรณ nแป™i dung ฤ‘แป™c hแบกi ฤ‘แป‘i vแป›i hร nh vi vi phแบกm quy ฤ‘แป‹nh tแบกi ฤiแปu nร y.'],
]
scores = model.predict(pairs)
print(scores)
# [1.3022e-05 1.2965e-05 1.7884e-03 2.5695e-04 1.2972e-05]

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'Nhรขn viรชn phแปฅc vแปฅ xe khรกch khรดng hแป— trแปฃ, giรบp ฤ‘แปก hร nh khรกch lร  ngฦฐแปi cao tuแป•i, trแบป em bแป‹ phแบกt bao nhiรชu?',
    [
        'ฤiแปu 59 - Luแบญt ฤฦฐแปng bแป™ : Quyแปn vร  nghฤฉa vแปฅ cแปงa ngฦฐแปi lรกi xe, nhรขn viรชn phแปฅc vแปฅ trรชn xe รด tรด kinh doanh vแบญn tแบฃi hร nh khรกch\n1. Tแปซ chแป‘i vแบญn tแบฃi hร nh khรกch cรณ mแป™t trong cรกc hร nh vi sau ฤ‘รขy:\na) Gรขy rแป‘i trแบญt tแปฑ cรดng cแป™ng trรชn xe รด tรด;\nb) Cแบฃn trแปŸ cรดng viแป‡c cแปงa ngฦฐแปi lรกi xe, nhรขn viรชn phแปฅc vแปฅ trรชn xe รด tรด;\nc) Gรขy แบฃnh hฦฐแปŸng ฤ‘แบฟn tรญnh mแบกng, sแปฉc khoแบป, tร i sแบฃn cแปงa ngฦฐแปi khรกc trรชn xe รด tรด;\nd) Gian lแบญn vรฉ;\nฤ‘) Vi phแบกm quy ฤ‘แป‹nh tแบกi ฤ‘iแปƒm c khoแบฃn 2 ฤiแปu 60 cแปงa Luแบญt nร y.\n2. Tแปซ chแป‘i ฤ‘iแปu khiแปƒn phฦฐฦกng tiแป‡n khi phรกt hiแป‡n phฦฐฦกng tiแป‡n khรดng bแบฃo ฤ‘แบฃm cรกc ฤ‘iแปu kiแป‡n vแป an toร n theo quy ฤ‘แป‹nh cแปงa phรกp luแบญt vแป trแบญt tแปฑ, an toร n giao thรดng ฤ‘ฦฐแปng bแป™.\n3. Khรดng ฤ‘ฦฐแปฃc tแปซ chแป‘i vแบญn tแบฃi hร nh khรกch, trแปซ lรฝ do quy ฤ‘แป‹nh tแบกi khoแบฃn 1 ฤiแปu nร y; khรดng ฤ‘ฦฐแปฃc gรขy khรณ khฤƒn ฤ‘แป‘i vแป›i hร nh khรกch lร  ngฦฐแปi cao tuแป•i, ngฦฐแปi khuyแบฟt tแบญt, trแบป em, phแปฅ nแปฏ mang thai; khรดng ฤ‘ฦฐแปฃc chuyแปƒn hร nh khรกch sang phฦฐฦกng tiแป‡n khรกc khi chฦฐa ฤ‘ฦฐแปฃc sแปฑ ฤ‘แป“ng รฝ cแปงa hร nh khรกch, trแปซ trฦฐแปng hแปฃp bแบฅt khแบฃ khรกng.\n4. Thu tiแปn ฤ‘รบng giรก vรฉ niรชm yแบฟt hoแบทc theo hแปฃp ฤ‘แป“ng vแบญn tแบฃi.\n5. Cรณ thรกi ฤ‘แป™, lแปi nรณi, hร nh vi vฤƒn minh, lแป‹ch sแปฑ; hฦฐแป›ng dแบซn hร nh khรกch ngแป“i ฤ‘รบng nฦกi quy ฤ‘แป‹nh, bแป‘ trรญ chแป— ngแป“i ฦฐu tiรชn cho ngฦฐแปi cao tuแป•i, ngฦฐแปi khuyแบฟt tแบญt, trแบป em vร  phแปฅ nแปฏ mang thai.\n6. Thแปฑc hiแป‡n trรกch nhiแป‡m theo quy ฤ‘แป‹nh cแปงa phรกp luแบญt vแป trแบญt tแปฑ, an toร n giao thรดng ฤ‘ฦฐแปng bแป™ vร  quy ฤ‘แป‹nh khรกc cแปงa phรกp luแบญt cรณ liรชn quan.',
        'ฤiแปu 18. Lแบฅy รฝ kiแบฟn tฦฐ vแบฅn cแปงa chuyรชn gia ฤ‘แป™c lแบญp vแป viแป‡c tuyแปƒn chแปn, giao trแปฑc tiแบฟp tแป• chแปฉc, cรก nhรขn chแปง trรฌ nhiแป‡m vแปฅ cแปงa Chฦฐฦกng trรฌnh : 1. ฤฦกn vแป‹ quแบฃn lรฝ nhiแป‡m vแปฅ cแปงa Chฦฐฦกng trรฌnh trรฌnh lรฃnh ฤ‘แบกo Bแป™ quyแบฟt ฤ‘แป‹nh lแปฑa chแปn 03 chuyรชn gia ฤ‘แป™c lแบญp ฤ‘แปƒ lแบฅy รฝ kiแบฟn tฦฐ vแบฅn vแป viแป‡c tuyแปƒn chแปn tแป• chแปฉc, tแป• chแปฉc chแปง trรฌ nhiแป‡m vแปฅ cแปงa Chฦฐฦกng trรฌnh.\n2. Chuyรชn gia ฤ‘แป™c lแบญp cรณ รฝ kiแบฟn tฦฐ vแบฅn bแบฑng vฤƒn bแบฃn sau khi ฤ‘รกnh giรก cรกc hแป“ sฦก tham gia tuyแปƒn chแปn, giao trแปฑc tiแบฟp tแป• chแปฉc, tแป• chแปฉc chแปง trรฌ nhiแป‡m vแปฅ. Chuyรชn gia ฤ‘แป™c lแบญp lร m viแป‡c ฤ‘แป™c lแบญp vร  chแป‹u trรกch nhiแป‡m trฦฐแป›c phรกp luแบญt vแป nhแบญn xรฉt, ฤ‘รกnh giรก vร  kแบฟt luแบญn cแปงa mรฌnh.\n3. ฤฦกn vแป‹ quแบฃn lรฝ nhiแป‡m vแปฅ cแปงa Chฦฐฦกng trรฌnh tแป• chแปฉc giao hแป“ sฦก cho cรกc chuyรชn gia ฤ‘แป™c lแบญp ฤ‘แปƒ thแปฑc hiแป‡n viแป‡c ฤ‘รกnh giรก lแบกi hแป“ sฦก cรกc nhiแป‡m vแปฅ tham gia tuyแปƒn chแปn, giao trแปฑc tiแบฟp. Thแปi gian ฤ‘รกnh giรก hแป“ sฦก tแป‘i ฤ‘a 15 ngร y kแปƒ tแปซ khi nhแบญn hแป“ sฦก.\n4. Hแป“ sฦก tuyแปƒn chแปn, giao trแปฑc tiแบฟp ฤ‘ฦฐแปฃc xem xรฉt phรช duyแป‡t nแบฟu cรณ รญt nhแบฅt 02 chuyรชn gia ฤ‘แป™c lแบญp ฤ‘รกnh giรก 70 ฤ‘iแปƒm trแปŸ lรชn.',
        'ฤiแปu 27. Quy trรฌnh chi tiแบฟt : 1. Chuแบฉn bแป‹ lแปฑa chแปn nhร  ฤ‘แบงu tฦฐ, bao gแป“m:\na) Lแบญp hแป“ sฦก mแปi thแบงu;\nb) Thแบฉm ฤ‘แป‹nh vร  phรช duyแป‡t hแป“ sฦก mแปi thแบงu.\n2. Tแป• chแปฉc lแปฑa chแปn nhร  ฤ‘แบงu tฦฐ, bao gแป“m:\na) Mแปi thแบงu;\nb) Phรกt hร nh, sแปญa ฤ‘แป•i, lร m rรต hแป“ sฦก mแปi thแบงu;\nc) Chuแบฉn bแป‹, nแป™p, tiแบฟp nhแบญn, quแบฃn lรฝ, sแปญa ฤ‘แป•i, rรบt hแป“ sฦก dแปฑ thแบงu;\nd) MแปŸ hแป“ sฦก ฤ‘แป xuแบฅt vแป kแปน thuแบญt.\n3. ฤรกnh giรก hแป“ sฦก ฤ‘แป xuแบฅt vแป kแปน thuแบญt, bao gแป“m:\na) Kiแปƒm tra, ฤ‘รกnh giรก tรญnh hแปฃp lแป‡ cแปงa hแป“ sฦก ฤ‘แป xuแบฅt vแป kแปน thuแบญt;\nb) ฤรกnh giรก chi tiแบฟt hแป“ sฦก ฤ‘แป xuแบฅt vแป kแปน thuแบญt;\nc) Thแบฉm ฤ‘แป‹nh, phรช duyแป‡t danh sรกch nhร  ฤ‘แบงu tฦฐ ฤ‘รกp แปฉng yรชu cแบงu vแป kแปน thuแบญt.\n4. MแปŸ vร  ฤ‘รกnh giรก hแป“ sฦก ฤ‘แป xuแบฅt vแป tร i chรญnh - thฦฐฦกng mแบกi, bao gแป“m:\na) MแปŸ hแป“ sฦก ฤ‘แป xuแบฅt vแป tร i chรญnh - thฦฐฦกng mแบกi;\nb) Kiแปƒm tra, ฤ‘รกnh giรก tรญnh hแปฃp lแป‡ cแปงa hแป“ sฦก ฤ‘แป xuแบฅt vแป tร i chรญnh - thฦฐฦกng mแบกi;\nc) ฤรกnh giรก chi tiแบฟt hแป“ sฦก ฤ‘แป xuแบฅt vแป tร i chรญnh - thฦฐฦกng mแบกi vร  xแบฟp hแบกng nhร  ฤ‘แบงu tฦฐ.\n5. Trรฌnh, thแบฉm ฤ‘แป‹nh, phรช duyแป‡t vร  cรดng khai kแบฟt quแบฃ lแปฑa chแปn nhร  ฤ‘แบงu tฦฐ.\n6. ฤร m phรกn, hoร n thiแป‡n, kรฝ kแบฟt hแปฃp ฤ‘แป“ng, bao gแป“m:\na) ฤร m phรกn, hoร n thiแป‡n hแปฃp ฤ‘แป“ng;\nb) Kรฝ kแบฟt hแปฃp ฤ‘แป“ng vร  cรดng khai thรดng tin hแปฃp ฤ‘แป“ng dแปฑ รกn.\n7. Trฦฐแปng hแปฃp dแปฑ รกn แปฉng dแปฅng cรดng nghแป‡ cao, quy trรฌnh lแปฑa chแปn nhร  ฤ‘แบงu tฦฐ thแปฑc hiแป‡n theo quy ฤ‘แป‹nh tแบกi cรกc khoแบฃn 1, 2, 3, 4 vร  5 ฤiแปu nร y. Nhร  ฤ‘แบงu tฦฐ ฤ‘ฦฐแปฃc lแปฑa chแปn sแบฝ tiแบฟn hร nh lแบญp bรกo cรกo nghiรชn cแปฉu khแบฃ thi. Trรชn cฦก sแปŸ bรกo cรกo nghiรชn cแปฉu khแบฃ thi ฤ‘ฦฐแปฃc duyแป‡t, cฦก quan nhร  nฦฐแป›c cรณ thแบฉm quyแปn vร  nhร  ฤ‘แบงu tฦฐ tiแบฟn hร nh ฤ‘ร m phรกn, hoร n thiแป‡n vร  kรฝ kแบฟt hแปฃp ฤ‘แป“ng. Nแป™i dung bรกo cรกo nghiรชn cแปฉu khแบฃ thi vร  hแปฃp ฤ‘แป“ng phแบฃi bแบฃo ฤ‘แบฃm phรน hแปฃp vแป›i mแปฅc tiรชu thแปฑc hiแป‡n dแปฑ รกn, quy mรด ฤ‘แบงu tฦฐ, cรกc tiรชu chรญ cฦก bแบฃn ฤ‘แปƒ xรฉt duyแป‡t nhร  ฤ‘แบงu tฦฐ trรบng thแบงu.',
        'ฤiแปu 28 - Luแบญt Giรกo dแปฅc : Cแบฅp hแปc vร  ฤ‘แป™ tuแป•i cแปงa giรกo dแปฅc phแป• thรดng\n\n1. Cรกc cแบฅp hแปc vร  ฤ‘แป™ tuแป•i cแปงa giรกo dแปฅc phแป• thรดng ฤ‘ฦฐแปฃc quy ฤ‘แป‹nh nhฦฐ sau:\n\na) Giรกo dแปฅc tiแปƒu hแปc ฤ‘ฦฐแปฃc thแปฑc hiแป‡n trong 05 nฤƒm hแปc, tแปซ lแป›p mแป™t ฤ‘แบฟn hแบฟt lแป›p nฤƒm. Tuแป•i cแปงa hแปc sinh vร o hแปc lแป›p mแป™t lร  06 tuแป•i vร  ฤ‘ฦฐแปฃc tรญnh theo nฤƒm;\n\nb) Giรกo dแปฅc trung hแปc cฦก sแปŸ ฤ‘ฦฐแปฃc thแปฑc hiแป‡n trong 04 nฤƒm hแปc, tแปซ lแป›p sรกu ฤ‘แบฟn hแบฟt lแป›p chรญn. Hแปc sinh vร o hแปc lแป›p sรกu phแบฃi hoร n thร nh chฦฐฦกng trรฌnh tiแปƒu hแปc. Tuแป•i cแปงa hแปc sinh vร o hแปc lแป›p sรกu lร  11 tuแป•i vร  ฤ‘ฦฐแปฃc tรญnh theo nฤƒm;\n\nc) Giรกo dแปฅc trung hแปc phแป• thรดng ฤ‘ฦฐแปฃc thแปฑc hiแป‡n trong 03 nฤƒm hแปc, tแปซ lแป›p mฦฐแปi ฤ‘แบฟn hแบฟt lแป›p mฦฐแปi hai. Hแปc sinh vร o hแปc lแป›p mฦฐแปi phแบฃi cรณ bแบฑng tแป‘t nghiแป‡p trung hแปc cฦก sแปŸ. Tuแป•i cแปงa hแปc sinh vร o hแปc lแป›p mฦฐแปi lร  15 tuแป•i vร  ฤ‘ฦฐแปฃc tรญnh theo nฤƒm.\n\n2. Trฦฐแปng hแปฃp hแปc sinh ฤ‘ฦฐแปฃc hแปc vฦฐแปฃt lแป›p, hแปc แปŸ ฤ‘แป™ tuแป•i cao hฦกn tuแป•i quy ฤ‘แป‹nh tแบกi khoแบฃn 1 ฤiแปu nร y bao gแป“m:\n\na) Hแปc sinh hแปc vฦฐแปฃt lแป›p trong trฦฐแปng hแปฃp phรกt triแปƒn sแป›m vแป trรญ tuแป‡;\n\nb) Hแปc sinh hแปc แปŸ ฤ‘แป™ tuแป•i cao hฦกn tuแป•i quy ฤ‘แป‹nh trong trฦฐแปng hแปฃp hแปc sinh hแปc lฦฐu ban, hแปc sinh แปŸ vรนng cรณ ฤ‘iแปu kiแป‡n kinh tแบฟ - xรฃ hแป™i ฤ‘แบทc biแป‡t khรณ khฤƒn, hแปc sinh lร  ngฦฐแปi dรขn tแป™c thiแปƒu sแป‘, hแปc sinh lร  ngฦฐแปi khuyแบฟt tแบญt, hแปc sinh kรฉm phรกt triแปƒn vแป thแปƒ lแปฑc hoแบทc trรญ tuแป‡, hแปc sinh mแป“ cรดi khรดng nฦกi nฦฐฦกng tแปฑa, hแปc sinh thuแป™c hแป™ nghรจo, hแปc sinh แปŸ nฦฐแป›c ngoร i vแป nฦฐแป›c vร  trฦฐแปng hแปฃp khรกc theo quy ฤ‘แป‹nh cแปงa phรกp luแบญt.\n\n3. Giรกo dแปฅc phแป• thรดng ฤ‘ฦฐแปฃc chia thร nh giai ฤ‘oแบกn giรกo dแปฅc cฦก bแบฃn vร  giai ฤ‘oแบกn giรกo dแปฅc ฤ‘แป‹nh hฦฐแป›ng nghแป nghiแป‡p. Giai ฤ‘oแบกn giรกo dแปฅc cฦก bแบฃn gแป“m cแบฅp tiแปƒu hแปc vร  cแบฅp trung hแปc cฦก sแปŸ; giai ฤ‘oแบกn giรกo dแปฅc ฤ‘แป‹nh hฦฐแป›ng nghแป nghiแป‡p lร  cแบฅp trung hแปc phแป• thรดng. Hoฬฃc sinh trong cฦก sแปŸ giรกo dแปฅc nghรชฬ€ nghiรชฬฃp ฤ‘ฦฐฦกฬฃc hoฬฃc khแป‘i lฦฐแปฃng kiรชฬn thฦฐฬc vฤƒn hรณa trung hแปc phรดฬ‰ thรดng.\n\n4. Bแป™ trฦฐแปŸng Bแป™ Giรกo dแปฅc vร  ฤร o tแบกo quy ฤ‘แป‹nh viแป‡c dแบกy vร  hแปc tiแบฟng Viแป‡t cho trแบป em lร  ngฦฐแปi dรขn tแป™c thiแปƒu sแป‘ trฦฐแป›c khi vร o hแปc lแป›p mแป™t; viแป‡c giแบฃng dแบกy khแป‘i lฦฐแปฃng kiแบฟn thฦฐฬc vฤƒn hoฬa trung hoฬฃc phรดฬ‰ thรดng trong cฦก sแปŸ giรกo dแปฅc nghแป nghiแป‡p; cรกc trฦฐแปng hแปฃp quy ฤ‘แป‹nh tแบกi khoแบฃn 2 ฤiแปu nร y.',
        'ฤiแปu 12. Hร nh vi vแบญn chuyแปƒn trรกi phรฉp hร ng hรณa, tiแปn tแป‡ qua biรชn giแป›i ngoร i khu vแปฑc cแปญa khแบฉu nhฦฐng chฦฐa ฤ‘แบฟn mแปฉc truy cแปฉu trรกch nhiแป‡m hรฌnh sแปฑ : 1. Phแบกt tiแปn tแปซ 1.000.000 ฤ‘แป“ng ฤ‘แบฟn 5.000.000 ฤ‘แป“ng ฤ‘แป‘i vแป›i hร nh vi vi phแบกm mร  tang vแบญt vi phแบกm cรณ giรก trแป‹ dฦฐแป›i 10.000.000 ฤ‘แป“ng.\n2. Phแบกt tiแปn tแปซ 5.000.000 ฤ‘แป“ng ฤ‘แบฟn 10.000.000 ฤ‘แป“ng ฤ‘แป‘i vแป›i mแป™t trong nhแปฏng hร nh vi sau:\na) Hร nh vi vi phแบกm mร  tang vแบญt vi phแบกm cรณ giรก trแป‹ tแปซ 10.000.000 ฤ‘แป“ng ฤ‘แบฟn dฦฐแป›i 50.000.000 ฤ‘แป“ng;\nb) Vแบญn chuyแปƒn trรกi phรฉp qua biรชn giแป›i hร ng hรณa lร  vฤƒn hรณa phแบฉm.\n3. Phแบกt tiแปn tแปซ 20.000.000 ฤ‘แป“ng ฤ‘แบฟn 40.000.000 ฤ‘แป“ng ฤ‘แป‘i vแป›i hร nh vi vi phแบกm mร  tang vแบญt vi phแบกm cรณ giรก trแป‹ tแปซ 50.000.000 ฤ‘แป“ng ฤ‘แบฟn dฦฐแป›i 100.000.000 ฤ‘แป“ng?\n4. Phแบกt tiแปn tแปซ 40.000.000 ฤ‘แป“ng ฤ‘แบฟn 50.000.000 ฤ‘แป“ng ฤ‘แป‘i vแป›i tang vแบญt vi phแบกm cรณ giรก trแป‹ tแปซ 100.000.000 ฤ‘แป“ng trแปŸ lรชn.\n5. Hรฌnh thแปฉc xแปญ phแบกt bแป• sung: Tแป‹ch thu tang vแบญt ฤ‘แป‘i vแป›i hร nh vi vi phแบกm quy ฤ‘แป‹nh tแบกi ฤiแปu nร y, trแปซ trฦฐแปng hแปฃp ฤ‘รฃ รกp dแปฅng biแป‡n phรกp khแบฏc phแปฅc hแบญu quแบฃ ฤ‘ฦฐแปฃc quy ฤ‘แป‹nh tแบกi khoแบฃn 6 ฤiแปu nร y.\n6. Biแป‡n phรกp khแบฏc phแปฅc hแบญu quแบฃ: Buแป™c tiรชu hแปงy hร ng hรณa, vแบญt phแบฉm gรขy hแบกi cho sแปฉc khแปe con ngฦฐแปi, vแบญt nuรดi, cรขy trแป“ng, mรดi trฦฐแปng vร  vฤƒn hรณa phแบฉm cรณ nแป™i dung ฤ‘แป™c hแบกi ฤ‘แป‘i vแป›i hร nh vi vi phแบกm quy ฤ‘แป‹nh tแบกi ฤiแปu nร y.',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Binary Classification

Metric Value
accuracy 0.9393
accuracy_threshold 0.8522
f1 0.7453
f1_threshold 0.2632
precision 0.75
recall 0.7406
average_precision 0.8104

Training Details

Training Dataset

Unnamed Dataset

  • Size: 22,828 training samples
  • Columns: sentence_0, sentence_1, and label
  • Approximate statistics based on the first 100 samples:
    sentence_0 sentence_1 label
    type string string float
    modality text text
    details
    • min: 10 tokens
    • mean: 26.94 tokens
    • max: 79 tokens
    • min: 41 tokens
    • mean: 313.04 tokens
    • max: 512 tokens
    • min: 0.0
    • mean: 0.15
    • max: 1.0
  • Samples:
    sentence_0 sentence_1 label
    Nhรขn viรชn phแปฅc vแปฅ xe khรกch khรดng hแป— trแปฃ, giรบp ฤ‘แปก hร nh khรกch lร  ngฦฐแปi cao tuแป•i, trแบป em bแป‹ phแบกt bao nhiรชu? ฤiแปu 59 - Luแบญt ฤฦฐแปng bแป™ : Quyแปn vร  nghฤฉa vแปฅ cแปงa ngฦฐแปi lรกi xe, nhรขn viรชn phแปฅc vแปฅ trรชn xe รด tรด kinh doanh vแบญn tแบฃi hร nh khรกch
    1. Tแปซ chแป‘i vแบญn tแบฃi hร nh khรกch cรณ mแป™t trong cรกc hร nh vi sau ฤ‘รขy:
    a) Gรขy rแป‘i trแบญt tแปฑ cรดng cแป™ng trรชn xe รด tรด;
    b) Cแบฃn trแปŸ cรดng viแป‡c cแปงa ngฦฐแปi lรกi xe, nhรขn viรชn phแปฅc vแปฅ trรชn xe รด tรด;
    c) Gรขy แบฃnh hฦฐแปŸng ฤ‘แบฟn tรญnh mแบกng, sแปฉc khoแบป, tร i sแบฃn cแปงa ngฦฐแปi khรกc trรชn xe รด tรด;
    d) Gian lแบญn vรฉ;
    ฤ‘) Vi phแบกm quy ฤ‘แป‹nh tแบกi ฤ‘iแปƒm c khoแบฃn 2 ฤiแปu 60 cแปงa Luแบญt nร y.
    2. Tแปซ chแป‘i ฤ‘iแปu khiแปƒn phฦฐฦกng tiแป‡n khi phรกt hiแป‡n phฦฐฦกng tiแป‡n khรดng bแบฃo ฤ‘แบฃm cรกc ฤ‘iแปu kiแป‡n vแป an toร n theo quy ฤ‘แป‹nh cแปงa phรกp luแบญt vแป trแบญt tแปฑ, an toร n giao thรดng ฤ‘ฦฐแปng bแป™.
    3. Khรดng ฤ‘ฦฐแปฃc tแปซ chแป‘i vแบญn tแบฃi hร nh khรกch, trแปซ lรฝ do quy ฤ‘แป‹nh tแบกi khoแบฃn 1 ฤiแปu nร y; khรดng ฤ‘ฦฐแปฃc gรขy khรณ khฤƒn ฤ‘แป‘i vแป›i hร nh khรกch lร  ngฦฐแปi cao tuแป•i, ngฦฐแปi khuyแบฟt tแบญt, trแบป em, phแปฅ nแปฏ mang thai; khรดng ฤ‘ฦฐแปฃc chuyแปƒn hร nh khรกch sang phฦฐฦกng tiแป‡n khรกc khi chฦฐa ฤ‘ฦฐแปฃc sแปฑ ฤ‘แป“ng รฝ cแปงa hร nh khรกch, trแปซ trฦฐแปng hแปฃp bแบฅt khแบฃ khรกng.
    4. Thu tiแปn ฤ‘รบng giรก vรฉ niรชm yแบฟt hoแบทc theo hแปฃp ฤ‘แป“ng vแบญn tแบฃi.
    5. C...
    0.0
    Trรฌnh tแปฑ vร  nแป™i dung lร m viแป‡c cแปงa hแป™i ฤ‘แป“ng tฦฐ vแบฅn tuyแปƒn chแปn, giao trแปฑc tiแบฟp tแป• chแปฉc chแปง trรฌ dแปฑ รกn Chฦฐฦกng trรฌnh phรกt triแปƒn tร i sแบฃn trรญ tuแป‡ ฤ‘ฦฐแปฃc quy ฤ‘แป‹nh nhฦฐ thแบฟ nร o? ฤiแปu 18. Lแบฅy รฝ kiแบฟn tฦฐ vแบฅn cแปงa chuyรชn gia ฤ‘แป™c lแบญp vแป viแป‡c tuyแปƒn chแปn, giao trแปฑc tiแบฟp tแป• chแปฉc, cรก nhรขn chแปง trรฌ nhiแป‡m vแปฅ cแปงa Chฦฐฦกng trรฌnh : 1. ฤฦกn vแป‹ quแบฃn lรฝ nhiแป‡m vแปฅ cแปงa Chฦฐฦกng trรฌnh trรฌnh lรฃnh ฤ‘แบกo Bแป™ quyแบฟt ฤ‘แป‹nh lแปฑa chแปn 03 chuyรชn gia ฤ‘แป™c lแบญp ฤ‘แปƒ lแบฅy รฝ kiแบฟn tฦฐ vแบฅn vแป viแป‡c tuyแปƒn chแปn tแป• chแปฉc, tแป• chแปฉc chแปง trรฌ nhiแป‡m vแปฅ cแปงa Chฦฐฦกng trรฌnh.
    2. Chuyรชn gia ฤ‘แป™c lแบญp cรณ รฝ kiแบฟn tฦฐ vแบฅn bแบฑng vฤƒn bแบฃn sau khi ฤ‘รกnh giรก cรกc hแป“ sฦก tham gia tuyแปƒn chแปn, giao trแปฑc tiแบฟp tแป• chแปฉc, tแป• chแปฉc chแปง trรฌ nhiแป‡m vแปฅ. Chuyรชn gia ฤ‘แป™c lแบญp lร m viแป‡c ฤ‘แป™c lแบญp vร  chแป‹u trรกch nhiแป‡m trฦฐแป›c phรกp luแบญt vแป nhแบญn xรฉt, ฤ‘รกnh giรก vร  kแบฟt luแบญn cแปงa mรฌnh.
    3. ฤฦกn vแป‹ quแบฃn lรฝ nhiแป‡m vแปฅ cแปงa Chฦฐฦกng trรฌnh tแป• chแปฉc giao hแป“ sฦก cho cรกc chuyรชn gia ฤ‘แป™c lแบญp ฤ‘แปƒ thแปฑc hiแป‡n viแป‡c ฤ‘รกnh giรก lแบกi hแป“ sฦก cรกc nhiแป‡m vแปฅ tham gia tuyแปƒn chแปn, giao trแปฑc tiแบฟp. Thแปi gian ฤ‘รกnh giรก hแป“ sฦก tแป‘i ฤ‘a 15 ngร y kแปƒ tแปซ khi nhแบญn hแป“ sฦก.
    4. Hแป“ sฦก tuyแปƒn chแปn, giao trแปฑc tiแบฟp ฤ‘ฦฐแปฃc xem xรฉt phรช duyแป‡t nแบฟu cรณ รญt nhแบฅt 02 chuyรชn gia ฤ‘แป™c lแบญp ฤ‘รกnh giรก 70 ฤ‘iแปƒm trแปŸ lรชn.
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    Cรกc bฦฐแป›c thแปฑc hiแป‡n viแป‡c mแปŸ vร  ฤ‘รกnh giรก hแป“ sฦก ฤ‘แป xuแบฅt vแป tร i chรญnh - thฦฐฦกng mแบกi cแปงa nhร  ฤ‘แบงu tฦฐ dแปฑ รกn ฤ‘แบงu tฦฐ cรณ sแปญ dแปฅng ฤ‘แบฅt ฤ‘ฦฐแปฃc quy ฤ‘แป‹nh nhฦฐ thแบฟ nร o? ฤiแปu 27. Quy trรฌnh chi tiแบฟt : 1. Chuแบฉn bแป‹ lแปฑa chแปn nhร  ฤ‘แบงu tฦฐ, bao gแป“m:
    a) Lแบญp hแป“ sฦก mแปi thแบงu;
    b) Thแบฉm ฤ‘แป‹nh vร  phรช duyแป‡t hแป“ sฦก mแปi thแบงu.
    2. Tแป• chแปฉc lแปฑa chแปn nhร  ฤ‘แบงu tฦฐ, bao gแป“m:
    a) Mแปi thแบงu;
    b) Phรกt hร nh, sแปญa ฤ‘แป•i, lร m rรต hแป“ sฦก mแปi thแบงu;
    c) Chuแบฉn bแป‹, nแป™p, tiแบฟp nhแบญn, quแบฃn lรฝ, sแปญa ฤ‘แป•i, rรบt hแป“ sฦก dแปฑ thแบงu;
    d) MแปŸ hแป“ sฦก ฤ‘แป xuแบฅt vแป kแปน thuแบญt.
    3. ฤรกnh giรก hแป“ sฦก ฤ‘แป xuแบฅt vแป kแปน thuแบญt, bao gแป“m:
    a) Kiแปƒm tra, ฤ‘รกnh giรก tรญnh hแปฃp lแป‡ cแปงa hแป“ sฦก ฤ‘แป xuแบฅt vแป kแปน thuแบญt;
    b) ฤรกnh giรก chi tiแบฟt hแป“ sฦก ฤ‘แป xuแบฅt vแป kแปน thuแบญt;
    c) Thแบฉm ฤ‘แป‹nh, phรช duyแป‡t danh sรกch nhร  ฤ‘แบงu tฦฐ ฤ‘รกp แปฉng yรชu cแบงu vแป kแปน thuแบญt.
    4. MแปŸ vร  ฤ‘รกnh giรก hแป“ sฦก ฤ‘แป xuแบฅt vแป tร i chรญnh - thฦฐฦกng mแบกi, bao gแป“m:
    a) MแปŸ hแป“ sฦก ฤ‘แป xuแบฅt vแป tร i chรญnh - thฦฐฦกng mแบกi;
    b) Kiแปƒm tra, ฤ‘รกnh giรก tรญnh hแปฃp lแป‡ cแปงa hแป“ sฦก ฤ‘แป xuแบฅt vแป tร i chรญnh - thฦฐฦกng mแบกi;
    c) ฤรกnh giรก chi tiแบฟt hแป“ sฦก ฤ‘แป xuแบฅt vแป tร i chรญnh - thฦฐฦกng mแบกi vร  xแบฟp hแบกng nhร  ฤ‘แบงu tฦฐ.
    5. Trรฌnh, thแบฉm ฤ‘แป‹nh, phรช duyแป‡t vร  cรดng khai kแบฟt quแบฃ lแปฑa chแปn nhร  ฤ‘แบงu tฦฐ.
    6. ฤร m phรกn, hoร n thiแป‡n, kรฝ kแบฟt hแปฃp ฤ‘แป“ng, bao gแป“m:
    a) ฤร m phรกn, hoร n thiแป‡n hแปฃ...
    0.0
  • Loss: BinaryCrossEntropyLoss with these parameters:
    {
        "activation_fn": "torch.nn.modules.linear.Identity",
        "pos_weight": null
    }
    

Training Hyperparameters

Non-Default Hyperparameters

  • per_device_train_batch_size: 16
  • per_device_eval_batch_size: 16

All Hyperparameters

Click to expand
  • per_device_train_batch_size: 16
  • num_train_epochs: 3
  • max_steps: -1
  • learning_rate: 5e-05
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: None
  • warmup_steps: 0
  • optim: adamw_torch_fused
  • optim_args: None
  • weight_decay: 0.0
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • optim_target_modules: None
  • gradient_accumulation_steps: 1
  • average_tokens_across_devices: True
  • max_grad_norm: 1
  • label_smoothing_factor: 0.0
  • bf16: False
  • fp16: False
  • bf16_full_eval: False
  • fp16_full_eval: False
  • tf32: None
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • torch_compile: False
  • torch_compile_backend: None
  • torch_compile_mode: None
  • use_liger_kernel: False
  • liger_kernel_config: None
  • use_cache: False
  • neftune_noise_alpha: None
  • torch_empty_cache_steps: None
  • auto_find_batch_size: False
  • log_on_each_node: True
  • logging_nan_inf_filter: True
  • include_num_input_tokens_seen: no
  • log_level: passive
  • log_level_replica: warning
  • disable_tqdm: False
  • project: huggingface
  • trackio_space_id: None
  • trackio_bucket_id: None
  • trackio_static_space_id: None
  • per_device_eval_batch_size: 16
  • prediction_loss_only: True
  • eval_on_start: False
  • eval_do_concat_batches: True
  • eval_use_gather_object: False
  • eval_accumulation_steps: None
  • include_for_metrics: []
  • batch_eval_metrics: False
  • save_only_model: False
  • save_on_each_node: False
  • enable_jit_checkpoint: False
  • push_to_hub: False
  • hub_private_repo: None
  • hub_model_id: None
  • hub_strategy: every_save
  • hub_always_push: False
  • hub_revision: None
  • load_best_model_at_end: False
  • ignore_data_skip: False
  • restore_callback_states_from_checkpoint: False
  • full_determinism: False
  • seed: 42
  • data_seed: None
  • use_cpu: False
  • accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
  • parallelism_config: None
  • dataloader_drop_last: False
  • dataloader_num_workers: 0
  • dataloader_pin_memory: True
  • dataloader_persistent_workers: False
  • dataloader_prefetch_factor: None
  • remove_unused_columns: True
  • label_names: None
  • train_sampling_strategy: random
  • length_column_name: length
  • ddp_find_unused_parameters: None
  • ddp_bucket_cap_mb: None
  • ddp_broadcast_buffers: False
  • ddp_static_graph: None
  • ddp_backend: None
  • ddp_timeout: 1800
  • fsdp: None
  • fsdp_config: None
  • deepspeed: None
  • debug: []
  • skip_memory_metrics: True
  • do_predict: False
  • resume_from_checkpoint: None
  • warmup_ratio: None
  • local_rank: -1
  • prompts: None
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: proportional
  • router_mapping: {}
  • learning_rate_mapping: {}

Training Logs

Epoch Step Training Loss ALQAC-Val_average_precision
0.3504 500 0.3142 0.6915
0.7008 1000 0.2368 0.7599
1.0 1427 - 0.7710
1.0512 1500 0.2117 0.7587
1.4015 2000 0.1728 0.7901
1.7519 2500 0.1587 0.7909
2.0 2854 - 0.8107
2.1023 3000 0.1394 0.8014
2.4527 3500 0.0981 0.8104

Training Time

  • Training: 24.6 minutes

Framework Versions

  • Python: 3.12.13
  • Sentence Transformers: 5.6.0
  • Transformers: 5.12.1
  • PyTorch: 2.12.0+cu130
  • Accelerate: 1.14.0
  • Datasets: 5.0.0
  • Tokenizers: 0.22.2

Additional Resources

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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