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---
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\"\
: \"[email protected]\",\n \"dallinger_email_address\": \"[email protected]\"\
,\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) <!-- at revision 744169034862c8eec56628663995004342e4e449 -->
- **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)
<!-- - **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': 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]
```
<!--
### 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.*
-->
<!--
## 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
#### 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: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | query | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 22.57 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 132.85 tokens</li><li>max: 1160 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 122.89 tokens</li><li>max: 1758 tokens</li></ul> |
* Samples:
| query | positive | negative |
|:-----------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>방학기간에 소외지역의 청소년을 대상으로 청춘누리 봉사단이 할 수 있는 캠프의 이름은 뭐야</code> | <code>주요 수상기관 교육기부프로그램 개요<br>4. 대학생 동아리 ｢청춘누리 봉사단｣<br>□ 청춘누리축제<br>◦ (참가대상) 전국 유치원, 초·중·고등학생<br>◦ (활동내역) 대학생들이 운영하는 교육기부활동을 청소년들이 직접 체험해봄으로써 학생들이 사고력, 창의력 향상을 도모하고 자신의 꿈을 펼칠 수 있는 장 마련<br>◦ (주요성과) 대학생들의 교육기부에 대한 전반적인 이해를 돕고 교육 기부 활동의 우수성 홍보<br>□ 청춘누리봉사단과 함께하는 교육기부(쏙쏙캠프, 함성소리)<br>◦ (참가대상) 전국의 초·중학생<br>◦ (활동내역)<br>- 쏙쏙캠프 : 방학을 이용하여 상대적으로 교육기부 혜택이 적은 소외 지역을 방문하여 창의력 체험, 진로체험 등을 제공, 배움의 기회 균등 및 꿈을 찾아주는 활동 전개<br>- 함성소리 : 학기중 토요일마다 수도권에 있는 청소년 대상으로 꿈을 설계하고 지원하는 활동 전개<br>◦ (주요성과) 소외지역 청소년 대상 배움의 기회를 제공하고 대학생들의 봉사활동을 장려하여 많은 청소년 대상 멘토 활동 전개</code> | <code>개도국에 IT나눔을 실천한 청년들과 아름다운 동행<br>□ 미래창조과학부(장관 최문기)와 한국정보화진흥원(원장 장광수)은 12월 18일(수) 오후 2시 10분 과천과학관에서 ｢2013년도 월드프렌즈 IT봉사단 귀국보고대회｣(이하, IT봉사단 귀국보고대회)를 개최하였다.<br>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.<br>※ 월드프렌즈(World Frends Korea, WFK) : 우리나라 해외봉사단사업 통합브랜드<br>□ 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.<br>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.</code> |
| <code>Loads sensor filters from an Excel file. Both new style XLSX and oldstyle XLS formats are supported.</code> | <code>def load_sensor_filters_excel(filename, normalise=False, sheet_names=None):<br><br> sensor_filters = {}<br> with pd.ExcelFile(filename) as excel_file:<br> # default is all sheets<br> if not sheet_names:<br> sheet_names = excel_file.sheet_names<br><br> for sheet in sheet_names:<br> try:<br> dataframe = excel_file.parse(<br> sheet, index_col=0<br> ) # the sheet as a DataFrame<br> # OK, we have the data frame. Let's process it...<br> if not _validate_filter_dataframe(dataframe):<br> continue<br><br> if normalise:<br> dataframe = _normalise_dataframe(dataframe)<br><br> sensor_filters[sheet] = (<br> np.array(dataframe.index),<br> dataframe.values.transpose(),<br> )<br><br> except xlrd.biffh.XLRDError:<br> continue<br> # except xlrd.biffh.XLRDError as xlrd_error:<br> # TODO: log wa...</code> | <code>def convert_csv(fname):<br><br> # Make sure this is an Excel file.<br> if (not is_excel_file(fname)):<br><br> # Not Excel, so no sheets.<br> return []<br><br> # Run soffice in listening mode if it is not already running.<br> run_soffice()<br> <br> # TODO: Make sure soffice is running in listening mode.<br> # <br> <br> # Connect to the local LibreOffice server.<br> context = connect(Socket(HOST, PORT))<br><br> # Load the Excel sheet.<br> component = get_component(fname, context)<br><br> # Iterate on all the sheets in the spreadsheet.<br> controller = component.getCurrentController()<br> sheets = component.getSheets()<br> enumeration = sheets.createEnumeration()<br> r = []<br> pos = 0<br> if sheets.getCount() > 0:<br> while enumeration.hasMoreElements():<br><br> # Move to next sheet.<br> sheet = enumeration.nextElement()<br> name = sheet.getName()<br> if (name.count(" ") > 10):<br> name = name.replace(" ", "")<br> name = fix_file_name(name)<br> ...</code> |
| <code>Create an additional feature to metadata by counting number of occurrences in data, for a specific element_type</code> | <code>def create_count_features(metadata, element_type, data, grp_feat, res_feat, feature_suffix):<br> feature_name = 'n_'+ element_type + '_modif' + feature_suffix<br> newfeature = (data.groupby([grp_feat])[res_feat]<br> .count()<br> .reset_index()<br> .fillna(0))<br> newfeature.columns = [grp_feat, feature_name]<br> metadata = pd.merge(metadata, newfeature, on=grp_feat, how="outer").fillna(0)<br> return metadata</code> | <code>def test(self):<br> count = Counter()<br> for example in self.testing_set:<br> classification = self.classify(example.attributes)<br><br> if example.CLASS and classification:<br> count['TP'] += 1<br> elif not example.CLASS and classification:<br> count['FP'] += 1<br> elif not example.CLASS and not classification:<br> count['TN'] += 1<br> elif example.CLASS and not classification:<br> count['FN'] += 1<br> return count</code> |
* Loss: [<code>CachedGISTEmbedLoss</code>](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
<details><summary>Click to expand</summary>
- `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
</details>
### Training Logs
<details><summary>Click to expand</summary>
| 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 |
</details>
### 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",
}
```
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