Introduction of SOCO-Java-codebert-mnrl-triplets-ep1-bs16-lr2e-05-split0.0
Model Details of SOCO-Java-codebert-mnrl-triplets-ep1-bs16-lr2e-05-split0.0
SentenceTransformer based on microsoft/codebert-base
This is a
sentence-transformers
model finetuned from
microsoft/codebert-base
on the
soco_train_java
dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
URLConnection conn = null;
private static boolean status = false;
public static void main (String args[]){
Dictionary a = new Dictionary();
String[] inp = {"
http://sec-crack.cs.rmit.edu./SEC/2/index.php
",
"",
""};
File file = new File("words");
exit:
try {
BufferedReader in = new BufferedReader(new FileReader(file));
int attempt = 0;
inp[2] = in.readLine();
while (inp[2] != null) {
if (inp[2].length() <= 3) {
attempt++;
a.doit(inp);
if (status) {
System.out.println("Crrect password is: " + inp[2]);
System.out.println("Number of attempts = " + attempt);
break exit;
}
}
inp[2] = in.readLine();
}
} catch (FileNotFoundException e1) {
URLConnection conn = null;
private static boolean status = false;
public static void main (String args[]){
Dictionary a = new Dictionary();
String[] inp = {"
http://sec-crack.cs.rmit.edu./SEC/2/index.php
",
"",
""};
File file = new File("words");
exit:
try {
BufferedReader in = new BufferedReader(new FileReader(file));
int attempt = 0;
inp[2] = in.readLine();
while (inp[2] != null) {
if (inp[2].length() <= 3) {
attempt++;
a.doit(inp);
if (status) {
System.out.println("Crrect password is: " + inp[2]);
System.out.println("Number of attempts = " + attempt);
break exit;
}
}
inp[2] = in.readLine();
}
} catch (FileNotFoundException e1) {
URLConnection conn = null;
private static boolean status = false;
public static void main (String args[]){
Dictionary a = new Dictionary();
String[] inp = {"
http://sec-crack.cs.rmit.edu./SEC/2/index.php
",
"",
""};
File file = new File("words");
exit:
try {
BufferedReader in = new BufferedReader(new FileReader(file));
int attempt = 0;
inp[2] = in.readLine();
while (inp[2] != null) {
if (inp[2].length() <= 3) {
attempt++;
a.doit(inp);
if (status) {
System.out.println("Crrect password is: " + inp[2]);
System.out.println("Number of attempts = " + attempt);
break exit;
}
}
inp[2] = in.readLine();
}
} catch (FileNotFoundException e1) {
@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",
}
MultipleNegativesRankingLoss
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Runs of buelfhood SOCO-Java-codebert-mnrl-triplets-ep1-bs16-lr2e-05-split0.0 on huggingface.co
126
Total runs
10
24-hour runs
31
3-day runs
16
7-day runs
45
30-day runs
More Information About SOCO-Java-codebert-mnrl-triplets-ep1-bs16-lr2e-05-split0.0 huggingface.co Model
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