File size: 72,313 Bytes
254faee |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 |
---
base_model: distilbert/distilbert-base-uncased-finetuned-sst-2-english
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:302
- loss:CosineSimilarityLoss
widget:
- source_sentence: "interface Input {\n id: number;\n title: string;\n parent_id:\
\ number | null; \n}\n\ninterface Output extends Input {\n children?: Output[];\
\ \n}\n\nfunction doJob(inputItems: Input[], parent_id?: number) {\n const outputItems:\
\ Output[] = [];\n\n for (let i = 0; i < inputItems.length; i++) {\n const\
\ children = doJob(inputItems.slice(i, inputItems.length), inputItems[i].parent_id)\n\
\ .filter(i => i.parent_id === parent_id);\n \n outputItems.push({...item,\
\ children});\n }\n\n return outputItems;\n}"
sentences:
- "interface Task {\n id: number;\n title: string;\n parent_id: number\
\ | null;\n children?: Task[];\n}\n\nfunction buildTaskTree(tasks: Task[]):\
\ Task[] {\n const tasksMap = tasks.reduce((acc, task) => {\n acc[task.id]\
\ = { ...task, children: [] };\n return acc;\n }, {} as { [key: number]:\
\ Task });\n\n const rootTasks: Task[] = [];\n\n tasks.forEach(task => {\n\
\ const { id, parent_id } = task;\n if (parent_id === null) {\n\
\ rootTasks.push(tasksMap[id]);\n } else {\n if (tasksMap[parent_id])\
\ {\n tasksMap[parent_id].children.push(tasksMap[id]);\n \
\ }\n }\n });\n\n return rootTasks;\n}\n\n// Test the function\
\ with the provided example\nconst inputTasks: Task[] = [\n { id: 1, title:\
\ 'Task 1', parent_id: null },\n { id: 2, title: 'Task 2', parent_id: 1 },\n\
\ { id: 3, title: 'Task 3', parent_id: 1 }\n];\nconst outputTasks: Task[] =\
\ buildTaskTree(inputTasks);\nconsole.log(outputTasks);\n"
- "const http = require('http');\n\nasync function checkUrlsStatus(urls) {\n \
\ const statusObj = {};\n\n const getStatus = async (url) => {\n return\
\ new Promise((resolve) => {\n http.get(url, (res) => {\n \
\ resolve(res.statusCode);\n }).on('error', (error) => {\n \
\ resolve(500); // Internal Server Error\n });\n \
\ });\n };\n\n await Promise.all(urls.map(async (url) => {\n const\
\ status = await getStatus(url);\n statusObj[url] = status;\n }));\n\
\n return statusObj;\n}\n\n// Example\nconst urls = ['https://example.com',\
\ 'https://google.com'];\ncheckUrlsStatus(urls)\n .then((result) => {\n \
\ console.log(result);\n })\n .catch((error) => {\n console.error(error);\n\
\ });\n\nmodule.exports = checkUrlsStatus;\n"
- "def find_longest_word(words):\n max_length = 0\n longest_word = ''\n\n\
\ for word in words:\n if len(word) > max_length:\n max_length\
\ = len(word)\n longest_word = word\n\n return longest_word, max_length\n\
\n# Test cases\nprint(find_longest_word(['hello', 'world', 'python', 'programming']))\
\ # Output: ('programming', 11)\nprint(find_longest_word(['short', 'longer',\
\ 'longest', 'size'])) # Output: ('longest', 7)\n"
- source_sentence: "// inventory.module.ts\nimport { Module } from '@nestjs/common';\n\
import { InventoryService } from './inventory.service';\nimport { InventoryController\
\ } from './inventory.controller';\nimport { TypeOrmModule } from '@nestjs/typeorm';\n\
import { Product } from './product.entity';\n@Module({\n imports: [TypeOrmModule.forFeature([Product])],\n\
\ providers: [InventoryService],\n controllers: [InventoryController],\n})\n\
export class InventoryModule {}\n// inventory.service.ts\nimport { Injectable\
\ } from '@nestjs/common';\nimport { InjectRepository } from '@nestjs/typeorm';\n\
import { Product } from './product.entity';\nimport { CreateProductDto, UpdateProductDto\
\ } from './product.dto';\n\n@Injectable()\nexport class InventoryService {\n\
\ constructor(\n @InjectRepository(Product)\n private readonly productRepository:\
\ Repository<Product>,\n ) {}\n\n async createProduct(createProductDto: CreateProductDto):\
\ Promise<Product> {\n const newProduct = new Product();\n newProduct.name\
\ = createProductDto.name;\n newProduct.description = createProductDto.description;\n\
\ newProduct.price = createProductDto.price;\n newProduct.availableQuantity\
\ = createProductDto.availableQuantity;\n\n return await this.productRepository.save(newProduct);\n\
\ }\n\n async updateProduct(\n productId: number,\n updateProductDto:\
\ UpdateProductDto,\n ): Promise<Product> {\n const product = await this.productRepository.findOne(productId);\n\
\ if (!product) {\n throw new NotFoundException('Product not found');\n\
\ }\n\n product.name = updateProductDto.name || product.name;\n product.description\
\ = updateProductDto.description || product.description;\n product.price =\
\ updateProductDto.price || product.price;\n product.availableQuantity =\n\
\ updateProductDto.availableQuantity || product.availableQuantity;\n\n \
\ return await this.productRepository.save(product);\n }\n\n async findAllProducts():\
\ Promise<Product[]> {\n return await this.productRepository.find();\n }\n\
\n async getProductById(productId: number): Promise<Product> {\n const product\
\ = await this.productRepository.findOne(productId);\n if (!product) {\n \
\ throw new NotFoundException('Product not found');\n }\n return product;\n\
\ }\n\n async checkProductAvailability(productId: number, quantity: number):\
\ Promise<boolean> {\n const product = await this.productRepository.findOne(productId);\n\
\ if (!product) {\n throw new NotFoundException('Product not found');\n\
\ }\n return product.availableQuantity >= quantity;\n }\n}"
sentences:
- "// inventory.dto.ts\nimport { IsInt, IsNotEmpty, IsNumber, IsString, Min } from\
\ 'class-validator';\n\nexport class ProductDto {\n @IsString()\n @IsNotEmpty()\n\
\ id: string;\n\n @IsString()\n @IsNotEmpty()\n name: string;\n\n @IsString()\n\
\ description: string;\n\n @IsNumber()\n @IsNotEmpty()\n price: number;\n\n\
\ @IsInt()\n @Min(0)\n @IsNotEmpty()\n availableQuantity: number;\n}\n\n//\
\ inventory.interface.ts\nexport interface Product {\n id: string;\n name: string;\n\
\ description: string;\n price: number;\n availableQuantity: number;\n}\n\n\
// inventory.module.ts\nimport { Module } from '@nestjs/common';\nimport { TypeOrmModule\
\ } from '@nestjs/typeorm';\nimport { InventoryController } from './inventory.controller';\n\
import { InventoryService } from './inventory.service';\nimport { Product } from\
\ './product.entity';\n\n@Module({\n imports: [TypeOrmModule.forFeature([Product])],\n\
\ controllers: [InventoryController],\n providers: [InventoryService]\n})\n\
export class InventoryModule {} \n\n// product.entity.ts\nimport { Entity, Column,\
\ PrimaryGeneratedColumn } from 'typeorm';\n\n@Entity()\nexport class Product\
\ {\n @PrimaryGeneratedColumn()\n id: number;\n\n @Column()\n name: string;\n\
\n @Column()\n description: string;\n\n @Column('decimal')\n price: number;\n\
\n @Column()\n availableQuantity: number;\n}\n\n// inventory.controller.ts\n\
import { Controller, Get, Post, Put, Body, Param } from '@nestjs/common';\nimport\
\ { InventoryService } from './inventory.service';\nimport { ProductDto } from\
\ './inventory.dto';\n\n@Controller('inventory')\nexport class InventoryController\
\ {\n constructor(private readonly inventoryService: InventoryService) {}\n\n\
\ @Post('add-product')\n async addProduct(@Body() productDto: ProductDto) {\n\
\ return this.inventoryService.addProduct(productDto);\n }\n\n @Get('products')\n\
\ async getProducts() {\n return this.inventoryService.getProducts();\n }\n\
\n @Put('update-quantity/:id')\n async updateQuantity(@Param('id') id: string,\
\ @Body('quantity') quantity: number) {\n return this.inventoryService.updateQuantity(id,\
\ quantity);\n }\n}\n\n// inventory.service.ts\nimport { Injectable } from '@nestjs/common';\n\
import { InjectRepository } from '@nestjs/typeorm';\nimport { Repository } from\
\ 'typeorm';\nimport { Product } from './product.entity';\nimport { ProductDto\
\ } from './inventory.dto';\n\n@Injectable()\nexport class InventoryService {\n\
\ constructor(\n @InjectRepository(Product)\n private productRepository:\
\ Repository<Product>,\n ) {}\n\n async addProduct(productDto: ProductDto):\
\ Promise<Product> {\n const newProduct = this.productRepository.create(productDto);\n\
\ return this.productRepository.save(newProduct);\n }\n\n async getProducts():\
\ Promise<Product[]> {\n return this.productRepository.find();\n }\n\n async\
\ updateQuantity(id: string, quantity: number): Promise<Product> {\n const\
\ product = await this.productRepository.findOne(id);\n if (!product) {\n \
\ throw new Error('Product not found');\n }\n\n product.availableQuantity\
\ = quantity;\n return this.productRepository.save(product);\n }\n}\n"
- "def move_zeros_to_end(lst):\n zero_count = 0\n for i in range(len(lst)):\n\
\ if lst[i] != 0:\n lst[i], lst[zero_count] = lst[zero_count],\
\ lst[i]\n zero_count += 1\n\n# Test cases\nlst1 = [0, 1, 0, 3, 12]\n\
move_zeros_to_end(lst1)\nprint(lst1) # Output: [1, 3, 12, 0, 0]\n\nlst2 = [0,\
\ 0, 1]\nmove_zeros_to_end(lst2)\nprint(lst2) # Output: [1, 0, 0]\n"
- "// inventory.dto.ts\nimport { IsInt, IsNotEmpty, IsNumber, IsString, Min } from\
\ 'class-validator';\n\nexport class ProductDto {\n @IsString()\n @IsNotEmpty()\n\
\ id: string;\n\n @IsString()\n @IsNotEmpty()\n name: string;\n\n @IsString()\n\
\ description: string;\n\n @IsNumber()\n @IsNotEmpty()\n price: number;\n\n\
\ @IsInt()\n @Min(0)\n @IsNotEmpty()\n availableQuantity: number;\n}\n\n//\
\ inventory.interface.ts\nexport interface Product {\n id: string;\n name: string;\n\
\ description: string;\n price: number;\n availableQuantity: number;\n}\n\n\
// inventory.module.ts\nimport { Module } from '@nestjs/common';\nimport { TypeOrmModule\
\ } from '@nestjs/typeorm';\nimport { InventoryController } from './inventory.controller';\n\
import { InventoryService } from './inventory.service';\nimport { Product } from\
\ './product.entity';\n\n@Module({\n imports: [TypeOrmModule.forFeature([Product])],\n\
\ controllers: [InventoryController],\n providers: [InventoryService]\n})\n\
export class InventoryModule {} \n\n// product.entity.ts\nimport { Entity, Column,\
\ PrimaryGeneratedColumn } from 'typeorm';\n\n@Entity()\nexport class Product\
\ {\n @PrimaryGeneratedColumn()\n id: number;\n\n @Column()\n name: string;\n\
\n @Column()\n description: string;\n\n @Column('decimal')\n price: number;\n\
\n @Column()\n availableQuantity: number;\n}\n\n// inventory.controller.ts\n\
import { Controller, Get, Post, Put, Body, Param } from '@nestjs/common';\nimport\
\ { InventoryService } from './inventory.service';\nimport { ProductDto } from\
\ './inventory.dto';\n\n@Controller('inventory')\nexport class InventoryController\
\ {\n constructor(private readonly inventoryService: InventoryService) {}\n\n\
\ @Post('add-product')\n async addProduct(@Body() productDto: ProductDto) {\n\
\ return this.inventoryService.addProduct(productDto);\n }\n\n @Get('products')\n\
\ async getProducts() {\n return this.inventoryService.getProducts();\n }\n\
\n @Put('update-quantity/:id')\n async updateQuantity(@Param('id') id: string,\
\ @Body('quantity') quantity: number) {\n return this.inventoryService.updateQuantity(id,\
\ quantity);\n }\n}\n\n// inventory.service.ts\nimport { Injectable } from '@nestjs/common';\n\
import { InjectRepository } from '@nestjs/typeorm';\nimport { Repository } from\
\ 'typeorm';\nimport { Product } from './product.entity';\nimport { ProductDto\
\ } from './inventory.dto';\n\n@Injectable()\nexport class InventoryService {\n\
\ constructor(\n @InjectRepository(Product)\n private productRepository:\
\ Repository<Product>,\n ) {}\n\n async addProduct(productDto: ProductDto):\
\ Promise<Product> {\n const newProduct = this.productRepository.create(productDto);\n\
\ return this.productRepository.save(newProduct);\n }\n\n async getProducts():\
\ Promise<Product[]> {\n return this.productRepository.find();\n }\n\n async\
\ updateQuantity(id: string, quantity: number): Promise<Product> {\n const\
\ product = await this.productRepository.findOne(id);\n if (!product) {\n \
\ throw new Error('Product not found');\n }\n\n product.availableQuantity\
\ = quantity;\n return this.productRepository.save(product);\n }\n}\n"
- source_sentence: "// wage-input.dto.ts\nimport { IsNumber, IsPositive } from 'class-validator';\n\
\nexport class WageInputDto {\n @IsNumber()\n @IsPositive()\n hourlyWage: number;\n\
\n @IsNumber()\n @IsPositive()\n hoursWorked: number;\n}\n\n// It will handle\
\ the input validation too.\n\n\n// employee.controller.ts\nimport { Body, Controller,\
\ Post } from '@nestjs/common';\nimport { WageInputDto } from './dto/wage-input.dto';\n\
import { EmployeeService } from './employee.service';\n\n@Controller('employee')\n\
export class EmployeeController {\n constructor(private readonly employeeService:\
\ EmployeeService) {}\n\n @Post('/wage')\n async getWage(@Body() input: WageInputDto)\
\ {\n return this.employeeService.getWage(input);\n }\n}\n\n// employee.service.ts\n\
import { Injectable } from '@nestjs/common';\nimport { WageInputDto } from './dto/wage-input.dto';\n\
\nconst WEEKLY_HOURS = 40;\n\n@Injectable()\nexport class EmployeeService {\n\
\ async getWage(input: WageInputDto) {\n let weeklyHours = 0;\n let overTimeHours\
\ = 0;\n let weeklyWage = 0;\n\n const hasDoneOverTime = input.hoursWorked\
\ > WEEKLY_HOURS;\n\n if (hasDoneOverTime) {\n weeklyHours = WEEKLY_HOURS;\n\
\ overTimeHours = input.hoursWorked - WEEKLY_HOURS;\n } else {\n \
\ weeklyHours = input.hoursWorked;\n }\n\n weeklyWage = weeklyHours * input.hourlyWage;\n\
\n if (hasDoneOverTime) {\n weeklyWage = weeklyWage + overTimeHours *\
\ (input.hourlyWage * 1.5);\n }\n\n return { weeklyWage };\n }\n}"
sentences:
- "import { Controller, Post, Body, HttpException, HttpStatus } from '@nestjs/common';\n\
\ninterface WeeklyWageInput {\n hourlyWage: number;\n hoursWorked: number;\n\
}\n\n@Controller('calculate-weekly-wage')\nexport class WeeklyWageController {\n\
\ @Post()\n calculateWeeklyWage(@Body() data: WeeklyWageInput): { weeklyWage:\
\ number } {\n // Input validation\n if (data.hourlyWage <= 0 || data.hoursWorked\
\ <= 0 || !Number.isInteger(data.hoursWorked)) {\n throw new HttpException('Invalid\
\ input. Hourly wage must be positive and hours worked must be a positive integer',\
\ HttpStatus.BAD_REQUEST);\n }\n\n const regularHours = Math.min(data.hoursWorked,\
\ 40);\n const overtimeHours = Math.max(data.hoursWorked - 40, 0);\n\n const\
\ weeklyWage = (regularHours * data.hourlyWage) + (overtimeHours * (1.5 * data.hourlyWage));\n\
\n return { weeklyWage };\n }\n}\n"
- "import { Pipe, PipeTransform } from '@angular/core';\n\n@Pipe({\n name: 'orderBy'\n\
})\nexport class OrderByPipe implements PipeTransform {\n transform(array: any[],\
\ key: string, order: 'asc' | 'desc'): any[] {\n if (!Array.isArray(array)\
\ || !key || (order !== 'asc' && order !== 'desc')) {\n console.error('Invalid\
\ input data');\n return array;\n }\n\n const compareFn = (a: any,\
\ b: any): number => {\n if (a[key] < b[key]) {\n return order ===\
\ 'asc' ? -1 : 1;\n }\n if (a[key] > b[key]) {\n return order\
\ === 'asc' ? 1 : -1;\n }\n return 0;\n };\n\n return array.slice().sort(compareFn);\n\
\ }\n}\n"
- "public class PalindromeChecker {\n public static boolean isPalindrome(String\
\ str) {\n str = str.toLowerCase().replaceAll(\"[^a-zA-Z0-9]\", \"\");\n\
\ int left = 0;\n int right = str.length() - 1;\n \n \
\ while (left < right) {\n if (str.charAt(left) != str.charAt(right))\
\ {\n return false;\n }\n left++;\n \
\ right--;\n }\n \n return true;\n }\n \n \
\ public static void main(String[] args) {\n String input1 = \"A man, a\
\ plan, a canal: Panama\";\n String input2 = \"race a car\";\n \n\
\ System.out.println(\"Input: '\" + input1 + \"' Output: \" + isPalindrome(input1));\n\
\ System.out.println(\"Input: '\" + input2 + \"' Output: \" + isPalindrome(input2));\n\
\ }\n}\n"
- source_sentence: 'FROM python:3.8
WORKDIR /app
COPY helloworld.py .
RUN pip install --no-cache-dir -r requirements.txt
CMD ["python", "helloworld.py"]
## PYTHON PROGRAM
helloworld.py
print("Hello, World!")
## BUILD COMMAND
docker build -t "python:helloworld" .
docker run -itd --name python python:helloworld'
sentences:
- '# Use a slim Python base image for optimization
FROM python:3.9-slim
# Set the working directory inside the container
WORKDIR /app
# Copy the Python script into the container
COPY hello.py /app/hello.py
# Define the command to run the Python script
CMD ["python", "/app/hello.py"]
'
- "import java.util.HashMap;\n\npublic class Solution {\n public int[] twoSum(int[]\
\ nums, int target) {\n HashMap<Integer, Integer> map = new HashMap<>();\n\
\n for (int i = 0; i < nums.length; i++) {\n int complement\
\ = target - nums[i];\n if (map.containsKey(complement)) {\n \
\ return new int[]{map.get(complement), i};\n }\n \
\ map.put(nums[i], i);\n }\n\n return new int[]{};\n }\n}\n\
\n// Example\nint[] array = new int[]{2, 7, 11, 15};\nint target = 9;\nSolution\
\ solution = new Solution();\nint[] result = solution.twoSum(array, target);\n"
- "function stripHtmlTags(input) {\n if (!input) return '';\n\n const tagRegex\
\ = /<[^>]*>/g;\n return input.replace(tagRegex, '');\n}\n"
- source_sentence: "def move_zeroes(nums):\n count = 0\n for i in range(len(nums)):\n\
\ if nums[i] != 0:\n nums[count], nums[i]= nums[i], nums[count]\n \
\ count += 1\n for i in range(count, len(nums)):\n nums[i] =0\n\ninput =\
\ [int(x) for x in input(\"Enter integers separated by spaces: \").split()]\n\
move_zeroes(input)\n\nprint(input)"
sentences:
- "import 'package:flutter/material.dart';\nimport 'package:firebase_core/firebase_core.dart';\n\
import 'package:firebase_auth/firebase_auth.dart';\nimport 'package:firebase_database/firebase_database.dart';\n\
\nvoid main() async {\n WidgetsFlutterBinding.ensureInitialized();\n await Firebase.initializeApp();\n\
\ runApp(MyApp());\n}\n\nclass MyApp extends StatelessWidget {\n final databaseRef\
\ = FirebaseDatabase.instance.reference().child('messages');\n\n @override\n\
\ Widget build(BuildContext context) {\n return MaterialApp(\n home:\
\ Scaffold(\n appBar: AppBar(\n title: Text('Real-Time Messages'),\n\
\ ),\n body: MessagesList(databaseRef: databaseRef),\n floatingActionButton:\
\ AddMessageButton(databaseRef: databaseRef),\n ),\n );\n }\n}\n\nclass\
\ MessagesList extends StatelessWidget {\n final DatabaseReference databaseRef;\n\
\n MessagesList({required this.databaseRef});\n\n @override\n Widget build(BuildContext\
\ context) {\n return StreamBuilder(\n stream: databaseRef.orderByChild('timestamp').onValue,\n\
\ builder: (context, snapshot) {\n if (snapshot.hasError) {\n \
\ return Text('Error: ${snapshot.error}');\n }\n\n if (!snapshot.hasData)\
\ {\n return Center(child: CircularProgressIndicator());\n }\n\
\n List<Message> messages = [];\n snapshot.data!.snapshot.value.forEach((key,\
\ value) {\n messages.add(Message.fromMap(value));\n });\n \
\ messages.sort((a, b) => a.timestamp.compareTo(b.timestamp));\n\n \
\ return ListView.builder(\n itemCount: messages.length,\n itemBuilder:\
\ (context, index) {\n return ListTile(\n title: Text(messages[index].text),\n\
\ );\n },\n );\n },\n );\n }\n}\n\nclass AddMessageButton\
\ extends StatelessWidget {\n final DatabaseReference databaseRef;\n\n AddMessageButton({required\
\ this.databaseRef});\n\n @override\n Widget build(BuildContext context) {\n\
\ return FloatingActionButton(\n onPressed: () {\n databaseRef.push().set({\n\
\ 'text': 'New Message',\n 'timestamp': DateTime.now().millisecondsSinceEpoch\n\
\ });\n },\n child: Icon(Icons.add),\n );\n }\n}\n\nclass\
\ Message {\n final String text;\n final int timestamp;\n\n Message({required\
\ this.text, required this.timestamp});\n\n factory Message.fromMap(Map<dynamic,\
\ dynamic> map) {\n return Message(\n text: map['text'],\n timestamp:\
\ map['timestamp'],\n );\n }\n}\n"
- "using System;\nusing System.Collections.Generic;\n\nclass BracketChecker\n{\n\
\ private readonly Dictionary<char, char> bracketPairs = new Dictionary<char,\
\ char>\n {\n { '(', ')' },\n { '[', ']' },\n { '{', '}'\
\ }\n };\n\n public bool CheckBalancedBrackets(string input)\n {\n \
\ if (string.IsNullOrEmpty(input))\n {\n return true;\n\
\ }\n\n Stack<char> stack = new Stack<char>();\n\n foreach\
\ (char c in input)\n {\n if (bracketPairs.ContainsValue(c))\n\
\ {\n if (stack.Count == 0 || bracketPairs[stack.Peek()]\
\ != c)\n {\n return false;\n \
\ }\n stack.Pop();\n }\n else if (bracketPairs.ContainsKey(c))\n\
\ {\n stack.Push(c);\n }\n }\n\n \
\ return stack.Count == 0;\n }\n}\n\nclass Program\n{\n static void\
\ Main()\n {\n BracketChecker bracketChecker = new BracketChecker();\n\
\n string input1 = \"(a+[b*c]-{d/e})\";\n Console.WriteLine(\"Input:\
\ \\\"{0}\\\"\", input1);\n Console.WriteLine(\"Output: {0}\\n\", bracketChecker.CheckBalancedBrackets(input1));\n\
\n string input2 = \"(a+[b*c)-{d/e}]\";\n Console.WriteLine(\"Input:\
\ \\\"{0}\\\"\", input2);\n Console.WriteLine(\"Output: {0}\", bracketChecker.CheckBalancedBrackets(input2));\n\
\ }\n}\n"
- "def move_zeros_to_end(lst):\n zero_count = 0\n for i in range(len(lst)):\n\
\ if lst[i] != 0:\n lst[i], lst[zero_count] = lst[zero_count],\
\ lst[i]\n zero_count += 1\n\n# Test cases\nlst1 = [0, 1, 0, 3, 12]\n\
move_zeros_to_end(lst1)\nprint(lst1) # Output: [1, 3, 12, 0, 0]\n\nlst2 = [0,\
\ 0, 1]\nmove_zeros_to_end(lst2)\nprint(lst2) # Output: [1, 0, 0]\n"
model-index:
- name: SentenceTransformer based on distilbert/distilbert-base-uncased-finetuned-sst-2-english
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: Unknown
type: unknown
metrics:
- type: pearson_cosine
value: 0.9000341656513303
name: Pearson Cosine
- type: spearman_cosine
value: 0.9013693287916293
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8619949591168187
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8020438201628594
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.868483180326987
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8234464507775442
name: Spearman Euclidean
- type: pearson_dot
value: 0.8494699061913786
name: Pearson Dot
- type: spearman_dot
value: 0.8947516297094024
name: Spearman Dot
- type: pearson_max
value: 0.9000341656513303
name: Pearson Max
- type: spearman_max
value: 0.9013693287916293
name: Spearman Max
---
# SentenceTransformer based on distilbert/distilbert-base-uncased-finetuned-sst-2-english
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english). 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.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [distilbert/distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english) <!-- at revision 714eb0fa89d2f80546fda750413ed43d93601a13 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("wasabibish/similarity-code-ai-generated")
# Run inference
sentences = [
'def move_zeroes(nums):\n count = 0\n for i in range(len(nums)):\n if nums[i] != 0:\n nums[count], nums[i]= nums[i], nums[count]\n count += 1\n for i in range(count, len(nums)):\n nums[i] =0\n\ninput = [int(x) for x in input("Enter integers separated by spaces: ").split()]\nmove_zeroes(input)\n\nprint(input)',
'def move_zeros_to_end(lst):\n zero_count = 0\n for i in range(len(lst)):\n if lst[i] != 0:\n lst[i], lst[zero_count] = lst[zero_count], lst[i]\n zero_count += 1\n\n# Test cases\nlst1 = [0, 1, 0, 3, 12]\nmove_zeros_to_end(lst1)\nprint(lst1) # Output: [1, 3, 12, 0, 0]\n\nlst2 = [0, 0, 1]\nmove_zeros_to_end(lst2)\nprint(lst2) # Output: [1, 0, 0]\n',
'using System;\nusing System.Collections.Generic;\n\nclass BracketChecker\n{\n private readonly Dictionary<char, char> bracketPairs = new Dictionary<char, char>\n {\n { \'(\', \')\' },\n { \'[\', \']\' },\n { \'{\', \'}\' }\n };\n\n public bool CheckBalancedBrackets(string input)\n {\n if (string.IsNullOrEmpty(input))\n {\n return true;\n }\n\n Stack<char> stack = new Stack<char>();\n\n foreach (char c in input)\n {\n if (bracketPairs.ContainsValue(c))\n {\n if (stack.Count == 0 || bracketPairs[stack.Peek()] != c)\n {\n return false;\n }\n stack.Pop();\n }\n else if (bracketPairs.ContainsKey(c))\n {\n stack.Push(c);\n }\n }\n\n return stack.Count == 0;\n }\n}\n\nclass Program\n{\n static void Main()\n {\n BracketChecker bracketChecker = new BracketChecker();\n\n string input1 = "(a+[b*c]-{d/e})";\n Console.WriteLine("Input: \\"{0}\\"", input1);\n Console.WriteLine("Output: {0}\\n", bracketChecker.CheckBalancedBrackets(input1));\n\n string input2 = "(a+[b*c)-{d/e}]";\n Console.WriteLine("Input: \\"{0}\\"", input2);\n Console.WriteLine("Output: {0}", bracketChecker.CheckBalancedBrackets(input2));\n }\n}\n',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Semantic Similarity
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:-------------------|:-----------|
| pearson_cosine | 0.9 |
| spearman_cosine | 0.9014 |
| pearson_manhattan | 0.862 |
| spearman_manhattan | 0.802 |
| pearson_euclidean | 0.8685 |
| spearman_euclidean | 0.8234 |
| pearson_dot | 0.8495 |
| spearman_dot | 0.8948 |
| pearson_max | 0.9 |
| **spearman_max** | **0.9014** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 302 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 302 samples:
| | sentence1 | sentence2 | score |
|:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 3 tokens</li><li>mean: 206.43 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 244.9 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.29</li><li>max: 0.9</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| <code>from django.views.generic import ListView<br><br>class PersonListView(ListView):<br> model = Person<br> template_name = 'person_list.html'<br><br> def get_queryset(self):<br> return Person.objects.filter(birthdate__year__lte=2005)</code> | <code>from myapp.models import Customer # Import the Customer model from your Django app<br><br>def get_customers_with_zip_code_starting_with_123():<br> customers = Customer.objects.filter(zip_code__startswith='123').values() # Query to filter customers with zip_code starting with '123'<br> return list(customers) # Return a list of dictionaries for matching records<br></code> | <code>0.4</code> |
| <code><div class="content-box"><br> <p>Welcome to our website!</p><br></div><br><style><br> .content-box {<br> margin: 20;<br> background-colour: #00G;<br> }<br></style></code> | <code>function createSentence(words, maxChars) {<br> if (words.length === 0 || maxChars < 1) {<br> return "";<br> }<br><br> let sentence = "";<br> let currentLength = 0;<br><br> for (let i = 0; i < words.length; i++) {<br> if (sentence.length + words[i].length + 1 <= maxChars) {<br> sentence += words[i] + " ";<br> currentLength += words[i].length + 1;<br> } else {<br> break;<br> }<br> }<br><br> if (sentence.length > 0) {<br> sentence = sentence.trim() + ".";<br> }<br><br> return sentence;<br>}<br><br>// Test the function with the example<br>const words = ['hello', 'world', 'this', 'is', 'a', 'test'];<br>const maxChars = 20;<br>console.log(createSentence(words, maxChars)); // Output: 'hello world this.'<br></code> | <code>0.1</code> |
| <code>AAAAAA</code> | <code>#include <atlstr.h><br>#include <vector><br><br>class KMP {<br>public:<br> std::vector<int> findPatternIndices(const CString& text, const CString& pattern) {<br> std::vector<int> indices;<br> if (pattern.IsEmpty() || text.IsEmpty()) {<br> return indices;<br> }<br><br> std::vector<int> lps = computeLPSArray(pattern);<br><br> int i = 0, j = 0;<br> while (i < text.GetLength()) {<br> if (pattern[j] == text[i]) {<br> j++;<br> i++;<br> }<br><br> if (j == pattern.GetLength()) {<br> indices.push_back(i - j);<br> j = lps[j - 1];<br> } else if (i < text.GetLength() && pattern[j] != text[i]) {<br> if (j != 0) {<br> j = lps[j - 1];<br> } else {<br> i++;<br> }<br> }<br> }<br><br> return indices;<br> }<br><br>private:<br> std::vector<int> computeLPSArray(const CString& pattern) {<br> int len = 0;<br> std::vector<int> lps(pattern.GetLength(), 0);<br> <br> int i = 1;<br> while (i < pattern.GetLength()) {<br> if (pattern[i] == pattern[len]) {<br> len++;<br> lps[i] = len;<br> i++;<br> } else {<br> if (len != 0) {<br> len = lps[len - 1];<br> } else {<br> lps[i] = 0;<br> i++;<br> }<br> }<br> }<br><br> return lps;<br> }<br>};<br><br>void testKMP() {<br> KMP kmp;<br> <br> CString text1 = "ABABDABACDABABCABAB";<br> CString pattern1 = "ABABCABAB";<br> std::vector<int> result1 = kmp.findPatternIndices(text1, pattern1);<br> OutputDebugString("Input: text='ABABDABACDABABCABAB', pattern='ABABCABAB' -> Output: [");<br> for (int i = 0; i < result1.size(); i++) {<br> OutputDebugString(result1[i]);<br> if (i < result1.size() - 1) {<br> OutputDebugString(",");<br> }<br> }<br> OutputDebugString("]\n");<br><br> CString text2 = "AAAAA";<br> CString pattern2 = "AAA";<br> std::vector<int> result2 = kmp.findPatternIndices(text2, pattern2);<br> OutputDebugString("Input: text='AAAAA', pattern='AAA' -> Output: [");<br> for (int i = 0; i < result2.size(); i++) {<br> OutputDebugString(result2[i]);<br> if (i < result2.size() - 1) {<br> OutputDebugString(",");<br> }<br> }<br> OutputDebugString("]\n");<br>}<br></code> | <code>0.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 76 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 76 samples:
| | sentence1 | sentence2 | score |
|:--------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 216.92 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 54 tokens</li><li>mean: 254.78 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.33</li><li>max: 0.9</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| <code>function stripHtmlTags(str) {<br> return str.replace(/<[^>]*>/g, '');<br>}<br><br>const input = '<p>Hello <em>World</em>!</p>';<br><br>const output = stripHtmlTags(input);<br><br>console.log(output);</code> | <code>function stripHtmlTags(input) {<br> if (!input) return '';<br><br> const tagRegex = /<[^>]*>/g;<br> return input.replace(tagRegex, '');<br>}<br></code> | <code>0.6</code> |
| <code><?php<br>function getTopThreeWords($text) {<br>// Remove punctuation and convert to lowercase<br>$words = str_word_count(strtolower(preg_replace('/[^\p{L}\p{N}\s]/u', ' ', $text)), 1);<br><br>// Count the frequency of each word<br>$wordFrequency = array_count_values($words);<br><br>// Sort the words by frequency in descending order<br>arsort($wordFrequency);<br><br>// Get the top three words<br>$topThreeWords = array_slice($wordFrequency, 0, 3, true);<br><br>// Format the output<br>$output = [];<br>foreach ($topThreeWords as $word => $count) {<br>$output[] = "('$word', $count)";<br>}<br><br>return '[' . implode(', ', $output) . ']';<br>}<br><br>// Example usage:<br>$inputText = "The quick brown fox jumps over the lazy dog. The dog was lazy!";<br>echo getTopThreeWords($inputText);<br>?></code> | <code><?php<br><br>function countTopWords($inputString) {<br> // Convert the input string to lowercase and remove punctuation<br> $cleanString = preg_replace("/[\W_]+/", " ", strtolower($inputString));<br><br> // Split the string into an array of words<br> $words = explode(" ", $cleanString);<br><br> // Count the frequency of each word<br> $wordCount = array_count_values($words);<br><br> // Sort the words by frequency in descending order<br> arsort($wordCount);<br><br> // Get the top three most common words<br> $topWords = array_slice($wordCount, 0, 3);<br><br> // Format the output as an array of tuples<br> $output = [];<br> foreach ($topWords as $word => $count) {<br> $output[] = [$word, $count];<br> }<br><br> return $output;<br>}<br><br>// Test the function with the example input<br>$inputString = "The quick brown fox jumps over the lazy dog. The dog was lazy!";<br>$output = countTopWords($inputString);<br>print_r($output);<br><br>?><br></code> | <code>0.3</code> |
| <code>AAAAAA</code> | <code>#include <atlstr.h><br>#include <vector><br><br>class KMP {<br>public:<br> std::vector<int> findPatternIndices(const CString& text, const CString& pattern) {<br> std::vector<int> indices;<br> if (pattern.IsEmpty() || text.IsEmpty()) {<br> return indices;<br> }<br><br> std::vector<int> lps = computeLPSArray(pattern);<br><br> int i = 0, j = 0;<br> while (i < text.GetLength()) {<br> if (pattern[j] == text[i]) {<br> j++;<br> i++;<br> }<br><br> if (j == pattern.GetLength()) {<br> indices.push_back(i - j);<br> j = lps[j - 1];<br> } else if (i < text.GetLength() && pattern[j] != text[i]) {<br> if (j != 0) {<br> j = lps[j - 1];<br> } else {<br> i++;<br> }<br> }<br> }<br><br> return indices;<br> }<br><br>private:<br> std::vector<int> computeLPSArray(const CString& pattern) {<br> int len = 0;<br> std::vector<int> lps(pattern.GetLength(), 0);<br> <br> int i = 1;<br> while (i < pattern.GetLength()) {<br> if (pattern[i] == pattern[len]) {<br> len++;<br> lps[i] = len;<br> i++;<br> } else {<br> if (len != 0) {<br> len = lps[len - 1];<br> } else {<br> lps[i] = 0;<br> i++;<br> }<br> }<br> }<br><br> return lps;<br> }<br>};<br><br>void testKMP() {<br> KMP kmp;<br> <br> CString text1 = "ABABDABACDABABCABAB";<br> CString pattern1 = "ABABCABAB";<br> std::vector<int> result1 = kmp.findPatternIndices(text1, pattern1);<br> OutputDebugString("Input: text='ABABDABACDABABCABAB', pattern='ABABCABAB' -> Output: [");<br> for (int i = 0; i < result1.size(); i++) {<br> OutputDebugString(result1[i]);<br> if (i < result1.size() - 1) {<br> OutputDebugString(",");<br> }<br> }<br> OutputDebugString("]\n");<br><br> CString text2 = "AAAAA";<br> CString pattern2 = "AAA";<br> std::vector<int> result2 = kmp.findPatternIndices(text2, pattern2);<br> OutputDebugString("Input: text='AAAAA', pattern='AAA' -> Output: [");<br> for (int i = 0; i < result2.size(); i++) {<br> OutputDebugString(result2[i]);<br> if (i < result2.size() - 1) {<br> OutputDebugString(",");<br> }<br> }<br> OutputDebugString("]\n");<br>}<br></code> | <code>0.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `weight_decay`: 0.2
- `max_steps`: 100
- `warmup_steps`: 150
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.2
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3.0
- `max_steps`: 100
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 150
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | loss | spearman_max |
|:------:|:----:|:------:|:------------:|
| 0.5263 | 20 | 0.3765 | 0.5421 |
| 1.0526 | 40 | 0.1518 | 0.5774 |
| 1.5789 | 60 | 0.0501 | 0.8533 |
| 2.1053 | 80 | 0.0217 | 0.8900 |
| 2.6316 | 100 | 0.0168 | 0.9014 |
### Framework Versions
- Python: 3.9.10
- Sentence Transformers: 3.1.0
- Transformers: 4.44.2
- PyTorch: 2.4.1+cpu
- Accelerate: 0.34.2
- Datasets: 3.0.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@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",
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |