| passages = [ | |
| "miHoYo - TECH OTAKUS SAVE THE WORLD", | |
| "Home Home", | |
| "Our Products Our Products", | |
| "About Us About Us", | |
| "Careers Careers", | |
| "CH", | |
| "EN", | |
| "Privacy Policy", | |
| "米哈游用户个人信息及隐私保护政策", |
| Audio path : common_voice_ja_21409740.mp3 | |
| Reference : 祖母は、おおむね機嫌よく、サイコロをころがしている。 | |
| Original model : ほぼ思い切りにおけさえコロコロはしている。 | |
| Finetuned model: 祖母 は おもい 機嫌 よく サイコロコロ か し て いる | |
| OpenAI model : ほぼ思い切りによってサイコロころがしている。 | |
| Audio path : common_voice_ja_22072759.mp3 | |
| Reference : 財布をなくしたので、交番へ行きます。 | |
| Original model : 財布をなくしたので交番へ行きます。 | |
| Finetuned model: 財布 を なくし た の て 交番 て 行き ます |
| -------< com.mycompany.hospital.management:hospital-management >-------- | |
| Building hospital-management 1.0-SNAPSHOT | |
| --------------------------------[ jar ]--------------------------------- | |
| --- exec-maven-plugin:3.0.0:exec (default-cli) @ hospital-management --- | |
| HỆ THỐNG QUẢN LÝ BỆNH VIỆN | |
| 1. Thêm y tá | |
| 2. Tìm kiếm y tá | |
| 3. Cập nhật y tá |
| from datasets import load_dataset, load_metric | |
| from transformers import AutoTokenizer | |
| from transformers import DataCollatorWithPadding | |
| from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer | |
| import numpy as np | |
| dataset = load_dataset('json', data_files=['data/train_qa_vi_mailong.jsonl']) | |
| checkpoint_name = "xlm-roberta-large" | |
| tokenizer = AutoTokenizer.from_pretrained(checkpoint_name) |
| import sys | |
| import re | |
| USAGE = """ | |
| python data_reader.py <input_file> | |
| """ | |
| """ | |
| File format: | |
| <pattern> | |
| <freq>3</freq> |
| #include <iostream> | |
| #include <utility> | |
| #include <queue> | |
| using namespace std; | |
| #define TRASH 1 | |
| #define VISITED 2 | |
| #define SIZE 3 | |
| int rooms[3][3] = { |
classification report threshold 0.1 precision recall f1-score support
accepted 0.97 0.75 0.85 8872
rejected 0.37 0.88 0.52 1505
accuracy 0.77 10377
macro avg 0.67 0.81 0.68 10377 weighted avg 0.89 0.77 0.80 10377
| 1 Ha Noi 3358.9 8418883 | |
| 2 Thanh pho Ho Chi Minh 2061 9411805 | |
| 3 Hai Phong 1561.8 2069110 | |
| 4 Da Nang 1284.9 1191381 | |
| 5 Ha Giang 7929.5 883388 | |
| 6 Cao Bang 6700.3 535098 | |
| 7 Lai Chau 9068.8 480588 | |
| 8 Lao Cai 6364 756083 | |
| 9 Tuyen Quang 5867.9 797392 | |
| 10 Lang Son 8310.2 791872 |
| #include <iostream> | |
| #include <thread> | |
| #include <ctime> | |
| void timer_task(int interval, bool &worker_idle){ | |
| std::thread t([=]() { | |
| while(1) { | |
| if (worker_idle == true) std::cout << "timer nha ... " << &worker_idle << "\n"; | |
| std::this_thread::sleep_for(std::chrono::milliseconds(interval)); | |
| } |
