--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:500000 - loss:CachedGISTEmbedLoss base_model: Qwen/Qwen3-Embedding-0.6B widget: - source_sentence: scramble z to retrieve negative samples, i.e. z values that should not be predicted by the model. sentences: - "def get_neg_z(self, z, cur_device):\n\n if self.opt.sampling_method ==\ \ 0:\n \"\"\"carefully selecting negative samples, such that they never\n\ \ include positive samples; done individually for every time-step -->\n\ \ very slow.\"\"\"\n offset = 1\n # generate\ \ uncorrelated negative samples by using an individual random\n # offset\ \ for every index\n rand_neg_idx = torch.arange(z.size(0), device=cur_device)\n\ \n rand_offset = (\n torch.multinomial(\n \ \ torch.ones(z.size(0) - offset),\n self.neg_samples\ \ * z.size(0),\n replacement=True,\n )\n \ \ + offset\n )\n rand_offset = rand_offset.reshape(self.neg_samples,\ \ -1).to(cur_device)\n\n z_neg = torch.stack(\n [\n\ \ torch.index_select(\n z, 0, (rand_neg_idx\ \ + rand_offset[i]) % z.size(0)\n )\n for\ \ i in range(self.neg_samples)\n ],\n 2,\n \ \ )\n elif self.opt.sampling_method == 1:\n \"\"\"randomly\ \ selecting from all z values.\n\n can cause positive samples to be\ \ selected as negative\n samples as well (but probability is <0.1%\ \ in our\n experiments) done once for all time-steps, much faster.\n\ \ \"\"\"\n z = self.broadcast_batch_length(z)\n \ \ z_neg = torch.stack(\n [\n torch.index_select(\n\ \ z, 0, torch.randperm(z.size(0), device=cur_device)\n\ \ )\n for i in range(self.neg_samples)\n\ \ ],\n 2,\n )\n rand_neg_idx\ \ = None\n rand_offset = None\n\n elif self.opt.sampling_method\ \ == 2:\n \"\"\"randomly selecting from z values within the same sequence.\n\ \n can cause positive samples to be selected as negative\n \ \ samples as well done once for all time-steps, much faster.\n \"\ \"\"\n z_neg = []\n channel = z.size(-1)\n batch_dim\ \ = z.size(0)\n seq_len = z.size(1)\n\n for _ in range(self.neg_samples):\n\ \ rand_perm_index = torch.randperm(\n batch_dim\ \ * seq_len, device=cur_device\n ).remainder_(seq_len)\n \ \ rand_perm_index = rand_perm_index.reshape(batch_dim, seq_len)\n \ \ batch_index_offset = (\n torch.arange(0, batch_dim,\ \ device=cur_device) * seq_len\n )\n rand_perm_index\ \ += batch_index_offset[:, None]\n\n z_neg.append(\n \ \ z.reshape(-1, channel)[rand_perm_index.view(-1)].reshape(\n \ \ batch_dim, seq_len, channel\n )\n \ \ )\n\n z_neg = torch.stack(z_neg, 3)\n\n rand_neg_idx\ \ = None\n rand_offset = None\n\n else:\n raise Exception(\"\ Invalid sampling_method option\")\n\n return z_neg, rand_neg_idx, rand_offset" - 마우스 전지방 3T3-L1세포주에 파이토케미칼을 조건에 따라 24시간 처리한 후 cell viability assay를 수행하였다. - "def _sample_neg(self, assign_result, num_expected):\n neg_inds = torch.nonzero(assign_result.gt_inds\ \ == 0)\n if neg_inds.numel() != 0:\n neg_inds = neg_inds.squeeze(1)\n\ \ if len(neg_inds) <= num_expected:\n return neg_inds\n \ \ elif self.neg_balance_thr <= 0:\n # uniform sampling among all\ \ negative samples\n return random_choice(neg_inds, num_expected)\n\ \ else:\n max_overlaps = assign_result.max_overlaps.cpu().numpy()\n\ \ # balance sampling for negative samples\n neg_set = set(neg_inds.cpu().numpy())\n\ \ easy_set = set(\n np.where(\n np.logical_and(max_overlaps\ \ >= 0,\n max_overlaps < self.neg_balance_thr))[0])\n\ \ hard_set = set(np.where(max_overlaps >= self.neg_balance_thr)[0])\n\ \ easy_neg_inds = list(easy_set & neg_set)\n hard_neg_inds\ \ = list(hard_set & neg_set)\n\n num_expected_hard = int(num_expected\ \ * self.neg_hard_fraction)\n if len(hard_neg_inds) > num_expected_hard:\n\ \ sampled_hard_inds = random_choice(hard_neg_inds,\n \ \ num_expected_hard)\n else:\n\ \ sampled_hard_inds = np.array(hard_neg_inds, dtype=np.int)\n \ \ num_expected_easy = num_expected - len(sampled_hard_inds)\n \ \ if len(easy_neg_inds) > num_expected_easy:\n sampled_easy_inds\ \ = random_choice(easy_neg_inds,\n \ \ num_expected_easy)\n else:\n sampled_easy_inds\ \ = np.array(easy_neg_inds, dtype=np.int)\n sampled_inds = np.concatenate((sampled_easy_inds,\n\ \ sampled_hard_inds))\n if\ \ len(sampled_inds) < num_expected:\n num_extra = num_expected\ \ - len(sampled_inds)\n extra_inds = np.array(list(neg_set - set(sampled_inds)))\n\ \ if len(extra_inds) > num_extra:\n extra_inds\ \ = random_choice(extra_inds, num_extra)\n sampled_inds = np.concatenate((sampled_inds,\ \ extra_inds))\n sampled_inds = torch.from_numpy(sampled_inds).long().to(\n\ \ assign_result.gt_inds.device)\n return sampled_inds" - source_sentence: if you wanted to know the mean and CI of m samples taken at a value x_val sentences: - "def predictSamples(m, x_val, x, y):\n n = len(x)\n x_mean = np.mean(x)\n yhat,\ \ upper, lower, stats = regression_with_CI(x, y)\n # mean at x_val:\n y_val\ \ = stats['a'] + stats['b'] * x_val\n # standard error of measurement at x_val\ \ for m samples:\n s_m = math.sqrt( stats['MS']*(1./m + 1./n + (x_val - x_mean)**2\ \ / \\\n stats['x_SS']) )\n t, stats = studentsT(x,\ \ y, stats)\n critval = returnCritValue(n-2)\n print('Mean for %i samples at\ \ %.3f: %.3f +/- %.3f' \n %(m, x_val, y_val, critval*s_m))\n return" - "async def resize_window(self, options):\n self.log_test(options['desc']\ \ if 'desc' in options else\n \"Resizing '\" + options['selector']\ \ + \"' window.\")\n\n # await self.page.screenshot({'path': 'preresize.png'})\n\ \n win_hndl = await self.get_handle(options['selector'])\n pre_resize_bbox\ \ = await win_hndl.boundingBox()\n\n edge_hndl = await self.get_handle(options['selector']\ \ + ' div.rsz-' + options['side'])\n edge_bbox = await edge_hndl.boundingBox()\n\ \n new_x = edge_bbox['x'] + \\\n resize_dirs[options['side']][0]\ \ * options['distance']\n new_y = edge_bbox['y'] + \\\n resize_dirs[options['side']][1]\ \ * options['distance']\n\n await edge_hndl.hover()\n await self.page.mouse.down()\n\ \ await self.page.mouse.move(new_x, new_y)\n await self.page.mouse.up()\n\ \n post_resize_bbox = await win_hndl.boundingBox()\n dw = post_resize_bbox['width']\ \ - pre_resize_bbox['width']\n dh = post_resize_bbox['height'] - pre_resize_bbox['height']\n\ \n resized = ((dw != 0) or (dh != 0))\n if options['expectChange']:\n\ \ self.assertIsNot(resized, False,\n \"\ The '\" + options['selector'] + \"' element was NOT resized and should have been.\"\ )\n else:\n self.assertIsNot(resized, True,\n \ \ \"The '\" + options['selector'] + \"' element was resized and\ \ should NOT have been.\")\n\n # await self.page.screenshot({'path': 'postresize.png'})" - "def _batch_stats(self, x):\n mu = torch.mean(x, dim=0, keepdim=True)\n\ \ var = torch.var(x, dim=0, keepdim=True)\n return mu, var" - source_sentence: 백악관은 도널드 트럼프 미국 대통령이 누구와 통화를 했다고 했어? sentences: - "def __str__(self):\n return '\\n'.join([self.header, self.sequence, self.header2,\ \ \n array('b', [x + self.qbase for x in self.quality]).tostring()])" - ' 백악관은 16일(현지시간) 미-중 정상이 전날 전화통화를 통해 최근 한반도 상황을 놓고 논의했다며 이같이 전했다.' - 도널드 트럼프 미국 대통령 - source_sentence: Return an example step handler for the given gym environemtn name, that uses the given config file. sentences: - "def stub_config():\n defaults = {\n \"activate_recruiter_on_start\"\ : True,\n \"ad_group\": \"Test ad group\",\n \"approve_requirement\"\ : 95,\n \"assign_qualifications\": True,\n \"auto_recruit\": True,\n\ \ \"aws_access_key_id\": \"fake aws key\",\n \"aws_secret_access_key\"\ : \"fake aws secret\",\n \"aws_region\": \"us-east-1\",\n \"base_payment\"\ : 0.01,\n \"base_port\": 5000,\n \"browser_exclude_rule\": \"MSIE,\ \ mobile, tablet\",\n \"clock_on\": False,\n \"contact_email_on_error\"\ : \"error_contact@test.com\",\n \"dallinger_email_address\": \"test@example.com\"\ ,\n \"database_size\": \"standard-0\",\n \"disable_when_duration_exceeded\"\ : True,\n \"enable_global_experiment_registry\": False,\n \"redis_size\"\ : \"premium-0\",\n \"dashboard_user\": \"admin\",\n \"database_url\"\ : \"postgresql://postgres@localhost/dallinger\",\n \"description\": \"\ fake HIT description\",\n \"duration\": 1.0,\n \"dyno_type\": \"\ free\",\n \"heroku_app_id_root\": \"fake-customid\",\n \"heroku_auth_token\"\ : \"heroku secret\",\n \"heroku_python_version\": \"3.9.2\",\n \"\ heroku_team\": \"\",\n \"host\": \"0.0.0.0\",\n \"id\": \"TEST_EXPERIMENT_UID\"\ , # This is a significant value; change with caution.\n \"keywords\":\ \ \"kw1, kw2, kw3\",\n \"lifetime\": 1,\n \"lock_table_when_creating_participant\"\ : True,\n \"logfile\": \"-\",\n \"loglevel\": 0,\n \"mode\"\ : \"debug\",\n \"num_dynos_web\": 1,\n \"num_dynos_worker\": 1,\n\ \ \"organization_name\": \"Monsters University\",\n \"sentry\":\ \ True,\n \"smtp_host\": \"smtp.fakehost.com:587\",\n \"smtp_username\"\ : \"fake email username\",\n \"smtp_password\": \"fake email password\"\ ,\n \"threads\": \"1\",\n \"title\": \"fake experiment title\",\n\ \ \"us_only\": True,\n \"webdriver_type\": \"chrome_headless\",\n\ \ \"whimsical\": True,\n \"replay\": False,\n \"worker_multiplier\"\ : 1.5,\n }\n from dallinger.config import Configuration, default_keys\n\n\ \ config = Configuration()\n for key in default_keys:\n config.register(*key)\n\ \ config.extend(defaults.copy())\n # Patch load() so we don't update any\ \ key/value pairs from actual files:\n config.load = mock.Mock(side_effect=lambda:\ \ setattr(config, \"ready\", True))\n config.ready = True\n\n return config" - 상부 챔버는 심방(또는 심실)이라고 불리며, 하부 챔버는 심실이라고 불립니다. 두 개의 심방은 심장으로 들어오는 혈액을 받는 챔버 역할을 하며, 더 근육질인 심실은 혈액을 심장에서 내보냅니다. - "def get_step_handler_for_gym_env(gym_env_name: str, cfg: Configuration) -> StepRewardDoneHandler:\r\ \n\r\n if gym_env_name == 'Acrobot-v1':\r\n handler = AcrobotStepHandler(cfg)\r\ \n elif gym_env_name == 'CartPole-v1':\r\n handler = CartPoleStepHandler(cfg)\r\ \n elif gym_env_name == 'MountainCarContinuous-v0':\r\n handler = ContinuousMountainCarStepHandler(cfg)\r\ \n elif gym_env_name == 'MountainCar-v0':\r\n handler = MountainCarStepHandler(cfg)\r\ \n elif gym_env_name == 'Pendulum-v0':\r\n handler = PendulumStepHandler(cfg)\r\ \n else:\r\n raise NotImplementedError(f'No support for this gym env:\ \ {gym_env_name}')\r\n\r\n return handler" - source_sentence: create list of spiders that obeys the visible projects list, through use of the spider selection menu sentences: - "def create_spiders_list():\n spiders_lst = [obj for obj in globals().values()\ \ if\n inspect.isclass(obj) and str(obj).split('.')[2] == 'spiders'\ \ and 'BaseSpider' not in str(obj)]\n visible_projects = find_visible_projects()\n\ \ spiders_dict = {i.split('.')[0]: [obj for obj in spiders_lst if i.split('.')[0]\ \ in str(obj)] for i in\n os.listdir('HousingPriceScraper/HousingPriceScraper/spiders/SpiderGroups')[:-1]\ \ if i.split('.')[0] in visible_projects}\n if len(list(spiders_dict.keys()))\ \ > 0:\n spiders_lst = select_spiders(spiders_dict)\n else:\n \ \ print('There are no visible projects, got to set_visible_projects to set defaults')\n\ \ return False\n return spiders_lst" - "def game(self, game_id=None, secret=None):\n if game_id is not None:\n\ \ self.game_id = game_id\n\n if secret is not None:\n \ \ self.secret = secret\n\n return self.game_id, self.secret" - "def instantiate_pipelines(settings, simulator_settings):\n pipelines = []\n\ \ # lock to manage race parallel processes race conditions \n lock = Lock()\n\ \n logger.info(\"\\nVALIDATING PIPELINES\\n\")\n for p_idx, pipeline_settings\ \ in enumerate(settings.runs):\n\n # turn a pipeline off by specifying\ \ num_runs as 0\n num_runs = pipeline_settings.get(\"num_runs\", 0)\n\n\ \ # start_idx determines the first dataset name's starting idx\n \ \ start_idx = pipeline_settings.get(\"start_idx\", 0)\n\n if num_runs:\n\ \ logger.info(\"Validating run: {}\\n\".format(p_idx))\n else:\n\ \ logger.info(\"Skipping run: {}\\n\".format(p_idx))\n \n\ \ for idx in range(start_idx, start_idx + num_runs): \n \ \ logger.info(\"Pipeline sub index: {}\\n\".format(idx))\n #\ \ class factory and instantiate pipeline object\n Pipeline = pipeline_factory(pipeline_settings[\"\ pipeline_name\"])\n p = Pipeline(pipeline_settings, idx, simulator_settings)\n\ \ \n # give each pipeline an idependent logger\n \ \ log_name = \"dSim_{}\".format(p.pipeline_settings[\"dataset_name\"])\n \ \ log_path = os.path.join(p.pipeline_settings[\"outdir\"],\n \ \ p.pipeline_settings[\"dataset_name\"]+'.log')\n\ \ fh = logging.FileHandler(log_path, mode='w')\n fh.setLevel(logging.DEBUG)\n\ \ format = \"%(asctime)-6s: %(name)s - %(levelname)s - %(message)s\"\ \n fmt = logging.Formatter(format)\n fh.setFormatter(fmt)\n\ \ local_logger = logging.getLogger(log_name)\n local_logger.addHandler(fh)\n\ \ logger.info(\"Init local logging: {}\".format(log_path))\n \ \ p.logger = local_logger\n\n # pipeline/ dataset directory\n\ \ p.pipeline_settings[\"lock\"] = lock\n\n # validate all\ \ submodules for each pipeline is ready (use local logger) \n p.instantiate_modules()\n\ \n # append to list of instantiated pipelines\n pipelines.append(p)\n\ \ return pipelines" datasets: - CocoRoF/massive_triplet_v3 pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer based on Qwen/Qwen3-Embedding-0.6B This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) on the [massive_triplet_v3](https://huggingface.co/datasets/CocoRoF/massive_triplet_v3) dataset. It maps sentences & paragraphs to a 1024-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:** [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) - **Maximum Sequence Length:** 32768 tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [massive_triplet_v3](https://huggingface.co/datasets/CocoRoF/massive_triplet_v3) ### 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': 32768, 'do_lower_case': False}) with Transformer model: Qwen3Model (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': True, 'include_prompt': True}) (2): Normalize() ) ``` ## 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("CocoRoF/POLAR-Qwen3-0.6b-linq-gist") # Run inference sentences = [ 'create list of spiders that obeys the visible projects list, through use of the spider selection menu', "def create_spiders_list():\n spiders_lst = [obj for obj in globals().values() if\n inspect.isclass(obj) and str(obj).split('.')[2] == 'spiders' and 'BaseSpider' not in str(obj)]\n visible_projects = find_visible_projects()\n spiders_dict = {i.split('.')[0]: [obj for obj in spiders_lst if i.split('.')[0] in str(obj)] for i in\n os.listdir('HousingPriceScraper/HousingPriceScraper/spiders/SpiderGroups')[:-1] if i.split('.')[0] in visible_projects}\n if len(list(spiders_dict.keys())) > 0:\n spiders_lst = select_spiders(spiders_dict)\n else:\n print('There are no visible projects, got to set_visible_projects to set defaults')\n return False\n return spiders_lst", 'def instantiate_pipelines(settings, simulator_settings):\n pipelines = []\n # lock to manage race parallel processes race conditions \n lock = Lock()\n\n logger.info("\\nVALIDATING PIPELINES\\n")\n for p_idx, pipeline_settings in enumerate(settings.runs):\n\n # turn a pipeline off by specifying num_runs as 0\n num_runs = pipeline_settings.get("num_runs", 0)\n\n # start_idx determines the first dataset name\'s starting idx\n start_idx = pipeline_settings.get("start_idx", 0)\n\n if num_runs:\n logger.info("Validating run: {}\\n".format(p_idx))\n else:\n logger.info("Skipping run: {}\\n".format(p_idx))\n \n for idx in range(start_idx, start_idx + num_runs): \n logger.info("Pipeline sub index: {}\\n".format(idx))\n # class factory and instantiate pipeline object\n Pipeline = pipeline_factory(pipeline_settings["pipeline_name"])\n p = Pipeline(pipeline_settings, idx, simulator_settings)\n \n # give each pipeline an idependent logger\n log_name = "dSim_{}".format(p.pipeline_settings["dataset_name"])\n log_path = os.path.join(p.pipeline_settings["outdir"],\n p.pipeline_settings["dataset_name"]+\'.log\')\n fh = logging.FileHandler(log_path, mode=\'w\')\n fh.setLevel(logging.DEBUG)\n format = "%(asctime)-6s: %(name)s - %(levelname)s - %(message)s"\n fmt = logging.Formatter(format)\n fh.setFormatter(fmt)\n local_logger = logging.getLogger(log_name)\n local_logger.addHandler(fh)\n logger.info("Init local logging: {}".format(log_path))\n p.logger = local_logger\n\n # pipeline/ dataset directory\n p.pipeline_settings["lock"] = lock\n\n # validate all submodules for each pipeline is ready (use local logger) \n p.instantiate_modules()\n\n # append to list of instantiated pipelines\n pipelines.append(p)\n return pipelines', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Training Details ### Training Dataset #### massive_triplet_v3 * Dataset: [massive_triplet_v3](https://huggingface.co/datasets/CocoRoF/massive_triplet_v3) at [51266de](https://huggingface.co/datasets/CocoRoF/massive_triplet_v3/tree/51266de17705934d628da7d4d9f74cc5f7b0b791) * Size: 500,000 training samples * Columns: query, positive, and negative * Approximate statistics based on the first 1000 samples: | | query | positive | negative | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | query | positive | negative | |:-----------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 방학기간에 소외지역의 청소년을 대상으로 청춘누리 봉사단이 할 수 있는 캠프의 이름은 뭐야 | 주요 수상기관 교육기부프로그램 개요
4. 대학생 동아리 ｢청춘누리 봉사단｣
□ 청춘누리축제
◦ (참가대상) 전국 유치원, 초·중·고등학생
◦ (활동내역) 대학생들이 운영하는 교육기부활동을 청소년들이 직접 체험해봄으로써 학생들이 사고력, 창의력 향상을 도모하고 자신의 꿈을 펼칠 수 있는 장 마련
◦ (주요성과) 대학생들의 교육기부에 대한 전반적인 이해를 돕고 교육 기부 활동의 우수성 홍보
□ 청춘누리봉사단과 함께하는 교육기부(쏙쏙캠프, 함성소리)
◦ (참가대상) 전국의 초·중학생
◦ (활동내역)
- 쏙쏙캠프 : 방학을 이용하여 상대적으로 교육기부 혜택이 적은 소외 지역을 방문하여 창의력 체험, 진로체험 등을 제공, 배움의 기회 균등 및 꿈을 찾아주는 활동 전개
- 함성소리 : 학기중 토요일마다 수도권에 있는 청소년 대상으로 꿈을 설계하고 지원하는 활동 전개
◦ (주요성과) 소외지역 청소년 대상 배움의 기회를 제공하고 대학생들의 봉사활동을 장려하여 많은 청소년 대상 멘토 활동 전개 | 개도국에 IT나눔을 실천한 청년들과 아름다운 동행
□ 미래창조과학부(장관 최문기)와 한국정보화진흥원(원장 장광수)은 12월 18일(수) 오후 2시 10분 과천과학관에서 ｢2013년도 월드프렌즈 IT봉사단 귀국보고대회｣(이하, IT봉사단 귀국보고대회)를 개최하였다.
o 정부는 2001년부터 현재까지 전 세계 70여개 개도국에 5,158명의 IT봉사단을 파견한 바 있으며, ｢IT봉사단 귀국보고대회｣는 매년 개도국에서 활동하고 온 봉사단원들이 서로의 경험을 공유하고 글로벌 역량을 배양하는 ‘소통'과 ‘협력‘의 장(場)으로 운영되고 있다.
※ 월드프렌즈(World Frends Korea, WFK) : 우리나라 해외봉사단사업 통합브랜드
□ 이번 ｢IT봉사단 귀국보고대회｣에는 30개국에 파견되었던 552명의 봉사단원 중 약 300여명의 봉사단원이 참석했으며, 윤종록 제2차관과 주한 외교사절(인도네시아 대사, 코스타리카 대사, 네팔 대사 등)이 참석해 세계의 오지를 누비고 온 봉사단원들을 격려했다.
o 윤종록 제2차관은 IT봉사단원들에게“귀한경험을 활용하여 대한민국의 이름을 빛내는 사람이 되기를 바란다”는 당부와 함께“정부는 여러분과 같은 젊은이들이 세계를 무대로 능력을 마음껏 발휘할 수 있는 글로벌 플랫폼을 구축하는데 노력할 계획”이라고 덧붙였다. | | Loads sensor filters from an Excel file. Both new style XLSX and oldstyle XLS formats are supported. | def load_sensor_filters_excel(filename, normalise=False, sheet_names=None):

sensor_filters = {}
with pd.ExcelFile(filename) as excel_file:
# default is all sheets
if not sheet_names:
sheet_names = excel_file.sheet_names

for sheet in sheet_names:
try:
dataframe = excel_file.parse(
sheet, index_col=0
) # the sheet as a DataFrame
# OK, we have the data frame. Let's process it...
if not _validate_filter_dataframe(dataframe):
continue

if normalise:
dataframe = _normalise_dataframe(dataframe)

sensor_filters[sheet] = (
np.array(dataframe.index),
dataframe.values.transpose(),
)

except xlrd.biffh.XLRDError:
continue
# except xlrd.biffh.XLRDError as xlrd_error:
# TODO: log wa... | def convert_csv(fname):

# Make sure this is an Excel file.
if (not is_excel_file(fname)):

# Not Excel, so no sheets.
return []

# Run soffice in listening mode if it is not already running.
run_soffice()

# TODO: Make sure soffice is running in listening mode.
#

# Connect to the local LibreOffice server.
context = connect(Socket(HOST, PORT))

# Load the Excel sheet.
component = get_component(fname, context)

# Iterate on all the sheets in the spreadsheet.
controller = component.getCurrentController()
sheets = component.getSheets()
enumeration = sheets.createEnumeration()
r = []
pos = 0
if sheets.getCount() > 0:
while enumeration.hasMoreElements():

# Move to next sheet.
sheet = enumeration.nextElement()
name = sheet.getName()
if (name.count(" ") > 10):
name = name.replace(" ", "")
name = fix_file_name(name)
... | | Create an additional feature to metadata by counting number of occurrences in data, for a specific element_type | def create_count_features(metadata, element_type, data, grp_feat, res_feat, feature_suffix):
feature_name = 'n_'+ element_type + '_modif' + feature_suffix
newfeature = (data.groupby([grp_feat])[res_feat]
.count()
.reset_index()
.fillna(0))
newfeature.columns = [grp_feat, feature_name]
metadata = pd.merge(metadata, newfeature, on=grp_feat, how="outer").fillna(0)
return metadata | def test(self):
count = Counter()
for example in self.testing_set:
classification = self.classify(example.attributes)

if example.CLASS and classification:
count['TP'] += 1
elif not example.CLASS and classification:
count['FP'] += 1
elif not example.CLASS and not classification:
count['TN'] += 1
elif example.CLASS and not classification:
count['FN'] += 1
return count | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 40960, 'do_lower_case': False}) with Transformer model: Qwen3Model (1): Pooling({'word_embedding_dimension': 4096, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': True, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.01} ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `overwrite_output_dir`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-06 - `weight_decay`: 0.01 - `adam_beta2`: 0.99 - `adam_epsilon`: 1e-07 - `max_grad_norm`: 0.3 - `num_train_epochs`: 1.0 - `warmup_ratio`: 0.1 - `dataloader_num_workers`: 16 - `hub_model_id`: CocoRoF/POLAR-Qwen3-0.6b-linq-gist - `prompts`: ({'query': 'Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery:', 'document': ''},) - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: True - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-06 - `weight_decay`: 0.01 - `adam_beta1`: 0.9 - `adam_beta2`: 0.99 - `adam_epsilon`: 1e-07 - `max_grad_norm`: 0.3 - `num_train_epochs`: 1.0 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `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`: True - `dataloader_num_workers`: 16 - `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} - `tp_size`: 0 - `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`: CocoRoF/POLAR-Qwen3-0.6b-linq-gist - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `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 - `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 - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: ({'query': 'Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery:', 'document': ''},) - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | |:------:|:----:|:-------------:| | 0.0082 | 1 | 2.0699 | | 0.0164 | 2 | 1.7826 | | 0.0246 | 3 | 1.9799 | | 0.0328 | 4 | 8.1569 | | 0.0410 | 5 | 4.641 | | 0.0492 | 6 | 4.847 | | 0.0573 | 7 | 8.2247 | | 0.0655 | 8 | 8.9525 | | 0.0737 | 9 | 4.2975 | | 0.0819 | 10 | 6.3088 | | 0.0901 | 11 | 5.6983 | | 0.0983 | 12 | 4.3867 | | 0.1065 | 13 | 6.1817 | | 0.1147 | 14 | 6.0226 | | 0.1229 | 15 | 15.2869 | | 0.1311 | 16 | 11.8965 | | 0.1393 | 17 | 9.4219 | | 0.1475 | 18 | 5.9216 | | 0.1557 | 19 | 6.5436 | | 0.1639 | 20 | 5.4599 | | 0.1720 | 21 | 4.6468 | | 0.1802 | 22 | 4.9366 | | 0.1884 | 23 | 4.5267 | | 0.1966 | 24 | 4.9044 | | 0.2048 | 25 | 4.9682 | | 0.2130 | 26 | 4.1537 | | 0.2212 | 27 | 4.0729 | | 0.2294 | 28 | 3.9093 | | 0.2376 | 29 | 3.3863 | | 0.2458 | 30 | 3.9228 | | 0.2540 | 31 | 2.8689 | | 0.2622 | 32 | 3.3243 | | 0.2704 | 33 | 2.7494 | | 0.2785 | 34 | 3.108 | | 0.2867 | 35 | 3.1585 | | 0.2949 | 36 | 3.2985 | | 0.3031 | 37 | 2.7137 | | 0.3113 | 38 | 2.8327 | | 0.3195 | 39 | 2.7932 | | 0.3277 | 40 | 3.038 | | 0.3359 | 41 | 2.769 | | 0.3441 | 42 | 2.7036 | | 0.3523 | 43 | 3.1873 | | 0.3605 | 44 | 2.5984 | | 0.3687 | 45 | 2.6836 | | 0.3769 | 46 | 3.0616 | | 0.3850 | 47 | 2.87 | | 0.3932 | 48 | 2.5225 | | 0.4014 | 49 | 2.3775 | | 0.4096 | 50 | 2.3407 | | 0.4178 | 51 | 2.6313 | | 0.4260 | 52 | 2.6966 | | 0.4342 | 53 | 2.3673 | | 0.4424 | 54 | 2.4391 | | 0.4506 | 55 | 2.5654 | | 0.4588 | 56 | 2.2967 | | 0.4670 | 57 | 2.4656 | | 0.4752 | 58 | 2.2497 | | 0.4834 | 59 | 2.3793 | | 0.4916 | 60 | 2.4427 | | 0.4997 | 61 | 2.2327 | | 0.5079 | 62 | 2.04 | | 0.5161 | 63 | 2.2881 | | 0.5243 | 64 | 2.0218 | | 0.5325 | 65 | 2.3258 | | 0.5407 | 66 | 2.1217 | | 0.5489 | 67 | 1.9639 | | 0.5571 | 68 | 2.1681 | | 0.5653 | 69 | 2.1941 | | 0.5735 | 70 | 2.1217 | | 0.5817 | 71 | 2.1097 | | 0.5899 | 72 | 2.1242 | | 0.5981 | 73 | 1.9071 | | 0.6062 | 74 | 1.8552 | | 0.6144 | 75 | 1.8398 | | 0.6226 | 76 | 1.9429 | | 0.6308 | 77 | 1.6457 | | 0.6390 | 78 | 1.656 | | 0.6472 | 79 | 1.6597 | | 0.6554 | 80 | 1.8188 | | 0.6636 | 81 | 2.0348 | | 0.6718 | 82 | 1.9511 | | 0.6800 | 83 | 1.8009 | | 0.6882 | 84 | 1.8279 | | 0.6964 | 85 | 1.7993 | | 0.7046 | 86 | 1.782 | | 0.7127 | 87 | 1.6168 | | 0.7209 | 88 | 1.7357 | | 0.7291 | 89 | 1.5588 | | 0.7373 | 90 | 1.6574 | | 0.7455 | 91 | 1.7124 | | 0.7537 | 92 | 1.7205 | | 0.7619 | 93 | 1.7439 | | 0.7701 | 94 | 1.4042 | | 0.7783 | 95 | 1.547 | | 0.7865 | 96 | 1.5815 | | 0.7947 | 97 | 1.4141 | | 0.8029 | 98 | 1.3568 | | 0.8111 | 99 | 1.5084 | | 0.8193 | 100 | 1.4027 | | 0.8274 | 101 | 1.4902 | | 0.8356 | 102 | 1.317 | | 0.8438 | 103 | 1.8041 | | 0.8520 | 104 | 1.4397 | | 0.8602 | 105 | 1.3406 | | 0.8684 | 106 | 1.5127 | | 0.8766 | 107 | 1.2449 | | 0.8848 | 108 | 1.4508 | | 0.8930 | 109 | 1.4171 | | 0.9012 | 110 | 1.626 | | 0.9094 | 111 | 1.285 | | 0.9176 | 112 | 1.2682 | | 0.9258 | 113 | 1.5178 | | 0.9339 | 114 | 1.3686 | | 0.9421 | 115 | 1.227 | | 0.9503 | 116 | 1.3685 | | 0.9585 | 117 | 1.3253 | | 0.9667 | 118 | 1.0893 | | 0.9749 | 119 | 1.1753 | | 0.9831 | 120 | 1.252 | | 0.9913 | 121 | 1.2304 | | 0.9995 | 122 | 1.1111 |
### Framework Versions - Python: 3.11.11 - Sentence Transformers: 3.4.1 - Transformers: 4.51.0 - PyTorch: 2.6.0+cu124 - Accelerate: 1.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.1 ## 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", } ```