sentence-transformers How to use digo-prayudha/test-modernbert-embed-base-miriad with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("digo-prayudha/test-modernbert-embed-base-miriad")
sentences = [
"First, states can clearly define which students are excluded from a cohort by transferring, and this definition should eliminate the possibility that a dropout will be counted as a transfer, as happens currently in Florida. Second, states should take steps to ensure the accuracy of a transfer code by requiring a transcript request or other confirmation step at the local level. Third, states should design an audit of the assignment of exit codes on an annual basis to ensure accuracy of the system as a whole, in addition to other editing and audit mechanisms. Fourth, states should group cohorts by birth year rather than the year in which they entered high school. There are several reasons for this last recommendation. Reporting graduation rates by birth cohort will eliminate the bias of differential retention rates. In addition, reporting graduation rates by birth cohort will eliminate any bias from differential placement of students transferring into a state's public high schools. With student-MIGRATION AND GRADUATION, PAGE 33 level databases, there is no significant cost to reporting graduation rates by birth cohort.\n\n Recently proposed grade-based graduation measures and a new age-based measure are all subject to bias from misestimating student migration, whether international, internal, or inter-sector. For one case, Virginia public schools in 2003, moving from an assumption of zero net migration or net-increment rates to 0.03 rates corresponds to changes in the graduation estimate between 6.6% and 10.7%, depending on the measure. In absolute terms, the various measures ranged from 63.2% to 83.5% given plausible net in-migration or net-increment rates between 0 and 0.03. Even relatively small changes in the assumed in-migration or net-increment rate, between 0.01 and 0.02, resulted in measurable drops of the graduation estimate between 2.2% and 3.6%, depending on the measure chosen. Florida's experience with longitudinal cohort graduation rates shows both the promise of the NGA compact on graduation rates and also the need for appropriate operationalization of definitions and steps to improve the technical adequacy of the information. Florida's rates are inflated because the graduation rate simultaneously eliminates responsibility for students who drop out and then immediately enroll in a GED program-and then credits public schools for the students who eventually earn a GED. Florida's database is also one with no confirmation or auditing of transfer codes.\n\n Finally, serious consideration needs to focus on the question of whether grade-based or age-based graduation rates are better. Most current school statistics report information by grade or grade cohort, including several recentlyproposed graduation-rate formulas and also the NGA compact and its progenitors (including Florida's official graduation rate). Yet grade-based graduation rates conflate grade level with cohort. Quasi-cohort methods that use ninth-grade enrollment statistics cannot distinguish first-time ninth-graders from repeaters.\n\n Longitudinal student databases such as Florida's cannot always determine the cohort to which a student transferring into the public schools truly belongs. Age is less vulnerable to such conflation problems, and any state with an accurate student database can report information by birth cohort (for longitudinal cohort rates) or by age (for period rates).\n\n Given the requirements in No Child Left Behind to calculate a graduation rate for every high school, it appears from the analysis here that there is no broadly-used measure currently able to estimate graduation with degree of precision at a state level, let alone at the school level. While the National Governors Association (2005c) compact on a longitudinal cohort rate is appropriate, at least in theory, in practice states that already have a longitudinal rate show some evidence of inflating graduation rates. The No Child Left Behind requirement is desirable but currently impossible to meet. Meeting the law requires a well-operated student registration system, a system where records of diplomas, enrollments, and transfers are all audited regularly to raise confidence in the accuracy of transfer and migration data.",
"What are the risks associated with bone cement leakage during a surgical procedure?\n",
"How can student migration impact graduation rate estimates?\n",
"What are the potential therapeutic effects of ShK-186 despite its short circulating half-life?\n"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
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