Instructions to use MinhPhuc0804/me5-256-kiem-tra-di-t1-v2.3-epoch-10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MinhPhuc0804/me5-256-kiem-tra-di-t1-v2.3-epoch-10 with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("MinhPhuc0804/me5-256-kiem-tra-di-t1-v2.3-epoch-10")

sentences = [
    "query: Travel restrictions showed up after the fact, but physical distancing, case isolation, & testing did the trick for #FlattenTheCurve. Our global crew's analysis (co-led by @user & @user) of data from >30,000 #COVID19 cases is out in @ScienceMagazine. 1/12",
    "passage: title: The effect of human mobility and control measures on the COVID-19 epidemic in China\nabstract: The ongoing coronavirus disease 2019 (COVID-19) outbreak expanded rapidly throughout China. Major behavioral, clinical, and state interventions were undertaken to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, affected COVID-19 spread in China. We used real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation in transmission in cities across China and to ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was explained well by human mobility data. After the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases were still indicative of local chains of transmission outside of Wuhan. This study shows that the drastic control measures implemented in China substantially mitigated the spread of COVID-19.",
    "passage: title: Reverse-transcribed SARS-CoV-2 RNA can integrate into the genome of cultured human cells and can be expressed in patient-derived tissues\nabstract: Prolonged detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA and recurrence of PCR-positive tests have been widely reported in patients after recovery from COVID-19, but some of these patients do not appear to shed infectious virus. We investigated the possibility that SARS-CoV-2 RNAs can be reverse-transcribed and integrated into the DNA of human cells in culture and that transcription of the integrated sequences might account for some of the positive PCR tests seen in patients. In support of this hypothesis, we found that DNA copies of SARS-CoV-2 sequences can be integrated into the genome of infected human cells.",
    "passage: title: Covid-19 and alcohol—a dangerous cocktail\nabstract: The principal aim of this work was to better understand how regenerating muscle fibers become innervated in adult animals. To induce muscle regeneration, individual identified muscle fibers in a mouse were damaged with a laser focused through a microscope. The muscle fiber that degenerated and the muscle fiber that was formed in its place were followed by viewing the same site repeatedly over a period of 2 d to 40 weeks. Commonly, the nerve terminal innervating the irradiated muscle fiber partially retracted during muscle fiber degeneration, and then sprouted to innervate the regenerating muscle fiber at the same site it had previously innervated the muscle fiber that was damaged. During the early phase of muscle regeneration we also observed sprouts originating from nerve terminals on adjacent muscle fibers. The new nerve growth was a response to the regenerating muscle fiber rather than to the degenerated fiber it replaced because repeated damage of the same site every 2–3 d over a 10 d period (to prevent regeneration) did not cause any sprouting."
]
embeddings = model.encode(sentences)

similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4]

Notebooks
Google Colab
Kaggle

me5-256-kiem-tra-di-t1-v2.3-epoch-10 / modules.json

MinhPhuc0804

Automated push: v3 Trainer, 256-seq, dev loss active

0d1ab53 verified about 1 month ago

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	[
	{
	"idx": 0,
	"name": "0",
	"path": "",
	"type": "sentence_transformers.base.modules.transformer.Transformer"
	},
	{
	"idx": 1,
	"name": "1",
	"path": "1_Pooling",
	"type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
	},
	{
	"idx": 2,
	"name": "2",
	"path": "2_Normalize",
	"type": "sentence_transformers.sentence_transformer.modules.normalize.Normalize"
	}
	]