--- tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:400 - loss:MultipleNegativesRankingLoss base_model: Qwen/Qwen3-Embedding-0.6B widget: - source_sentence: "Wrapper for calling the clean method of services attribute\n\n\ \ :return: None" sentences: - "def import_from_nhmmer_table(hmmout_path):\n \n \n \n \ \ \n res=HMMSearchResult()\n res.fields = [\n \ \ SequenceSearchResult.QUERY_ID_FIELD,\n SequenceSearchResult.HMM_NAME_FIELD,\n\ \ SequenceSearchResult.ALIGNMENT_LENGTH_FIELD,\n \ \ SequenceSearchResult.QUERY_FROM_FIELD,\n \ \ SequenceSearchResult.QUERY_TO_FIELD,\n SequenceSearchResult.HIT_FROM_FIELD,\n\ \ SequenceSearchResult.HIT_TO_FIELD,\n \ \ SequenceSearchResult.ALIGNMENT_BIT_SCORE,\n SequenceSearchResult.ALIGNMENT_DIRECTION,\n\ \ ]\n \n for row in [x.rstrip().split() for\ \ x in open(hmmout_path) if not x.startswith()]:\n alifrom = int(row[6])\n\ \ alito = int(row[7])\n aln_length = (alito-alifrom\ \ if alito-alifrom>0 else alifrom-alito)\n res.results.append([row[0],\n\ \ row[2],\n aln_length,\n\ \ int(row[4]),\n \ \ int(row[5]),\n alifrom,\n \ \ alito,\n row[13],\n \ \ alito > alifrom\n ])\n \ \ return res" - "def clean(self):\n \n logger.debug(\"Cleaning configuration objects\ \ before configuration sending:\")\n types_creations = self.__class__.types_creations\n\ \ for o_type in types_creations:\n (_, _, inner_property, _,\ \ _) = types_creations[o_type]\n logger.debug(\" . for %s\", inner_property,\ \ )\n inner_object = getattr(self, inner_property)\n inner_object.clean()" - "def index_modules(idx=None, path=None):\n \n suppress_output()\n modules\ \ = defaultdict(list)\n pkglist = pkgutil.walk_packages(onerror=lambda x: True)\n\ \ print(pkglist)\n if path:\n pkglist = pkgutil.walk_packages(path,\ \ onerror=lambda x: True)\n for modl, name, ispkg in pkglist:\n try:\n\ \ path = os.path.join(modl.path, name.split()[-1])\n except\ \ AttributeError:\n \n continue\n\n if os.path.isdir(path):\n\ \ path = os.path.join(path, )\n path += \n\n objs = []\n\ \n if os.path.exists(path):\n try:\n objs = read_objs_from_path(path)\n\ \ except:\n continue\n elif not re.search(MODULE_BLACKLIST,\ \ name):\n try:\n mod = __import__(name)\n \ \ objs = [k for k in dir(mod) if not k.startswith()]\n except:\n\ \ continue\n else:\n continue\n\n for\ \ obj in objs:\n if name not in modules[obj]:\n modules[obj].append(name)\n\ \ suppress_output(True)\n return merge_dicts(idx, dict(modules))" - source_sentence: Try to import the aeneas package and return ``True`` if that fails. sentences: - "def check_import():\n \n try:\n import aeneas\n print_success(u\"\ aeneas OK\")\n return False\n except ImportError:\n print_error(u\"\ aeneas ERROR\")\n print_info(u\" Unable to load the aeneas Python\ \ package\")\n print_info(u\" This error is probably caused by:\")\n \ \ print_info(u\" A. you did not download/git-clone the aeneas package\ \ properly; or\")\n print_info(u\" B. you did not install the required\ \ Python packages:\")\n print_info(u\" 1. BeautifulSoup4\")\n \ \ print_info(u\" 2. lxml\")\n print_info(u\" 3. numpy\")\n\ \ except Exception as e:\n print_error(e)\n return True" - "def simplify(source, kink=20):\n \n source_coord = map(lambda o: {\"lng\"\ : o.coordinates[0], \"lat\": o.coordinates[1]}, source)\n\n \n \n \n\ \ F = (math.pi / 180.0) * 0.5\n index = [] \n sig_start = [] \n sig_end\ \ = []\n\n \n count = len(source_coord)\n if count < 3:\n return\ \ source_coord \n\n \n\n band_sqr = kink * 360.0 / (2.0 * math.pi * 6378137.0)\ \ \n band_sqr *= band_sqr\n n_dest = 0\n sig_start[0] = 0\n sig_end[0]\ \ = count - 1\n n_stack = 1\n\n \n while n_stack > 0:\n \n \ \ start = sig_start[n_stack - 1]\n end = sig_end[n_stack - 1]\n \ \ n_stack -= 1\n\n if (end - start) > 1: \n \n \ \ x12 = source[end][\"lng\"] - source[start][\"lng\"]\n y12 = source[end][\"\ lat\"] - source[start][\"lat\"]\n if math.fabs(x12) > 180.0:\n \ \ x12 = 360.0 - math.fabs(x12)\n x12 *= math.cos(F * (source[end][\"\ lat\"] + source[start][\"lat\"])) \n d12 = (x12 * x12) + (y12 * y12)\n\ \n i = start + 1\n sig = start\n max_dev_sqr\ \ = -1.0\n while i < end:\n x13 = source[i][\"lng\"\ ] - source[start][\"lng\"]\n y13 = source[i][\"lat\"] - source[start][\"\ lat\"]\n if math.fabs(x13) > 180.0:\n x13 =\ \ 360.0 - math.fabs(x13)\n x13 *= math.cos(F * (source[i][\"lat\"\ ] + source[start][\"lat\"]))\n d13 = (x13 * x13) + (y13 * y13)\n\ \ x23 = source[i][\"lng\"] - source[end][\"lng\"]\n \ \ y23 = source[i][\"lat\"] - source[end][\"lat\"]\n if math.fabs(x23)\ \ > 180.0:\n x23 = 360.0 - math.fabs(x23)\n \ \ x23 *= math.cos(F * (source[i][\"lat\"] + source[end][\"lat\"]))\n \ \ d23 = (x23 * x23) + (y23 * y23)\n\n if d13 >= (d12 + d23):\n\ \ dev_sqr = d23\n elif d23 >= (d12 + d13):\n\ \ dev_sqr = d13\n else:\n \ \ dev_sqr = (x13 * y12 - y13 * x12) * (x13 * y12 - y13 * x12) / d12 \n \ \ if dev_sqr > max_dev_sqr:\n sig = i\n \ \ max_dev_sqr = dev_sqr\n i += 1\n\n\n if max_dev_sqr\ \ < band_sqr: \n \n index[n_dest] = start\n \ \ n_dest += 1\n else: \n n_stack += 1\n \ \ sig_start[n_stack - 1] = sig\n sig_end[n_stack - 1]\ \ = end\n n_stack += 1\n sig_start[n_stack - 1]\ \ = start\n sig_end[n_stack - 1] = sig\n\n else: \n \ \ index[n_dest] = start\n n_dest += 1\n\n \n index[n_dest]\ \ = count - 1\n n_dest += 1\n\n \n r = []\n for i in range(0, n_dest):\n\ \ r.append(source_coord[index[i]])\n\n return map(lambda o: {\"type\"\ : \"Point\",\"coordinates\": [o.lng, o.lat]}, r)" - "def smooth(data, fw):\r\n \r\n if fw == 0:\r\n fdata = data\r\n\ \ else:\r\n fdata = lfilter(np.ones(fw)/fw, 1, data)\r\n return fdata" - source_sentence: Start response processing. sentences: - "async def start(self, connection: ) -> :\n \n self._closed = False\n\ \ self._protocol = connection.protocol\n self._connection = connection\n\ \n with self._timer:\n while True:\n \n \ \ try:\n message, payload = await self._protocol.read()\ \ \n except http.HttpProcessingError as exc:\n \ \ raise ClientResponseError(\n self.request_info, self.history,\n\ \ status=exc.code,\n message=exc.message,\ \ headers=exc.headers) from exc\n\n if (message.code < 100 or\n\ \ message.code > 199 or message.code == 101):\n \ \ break\n\n if self._continue is not None:\n \ \ set_result(self._continue, True)\n self._continue\ \ = None\n\n \n payload.on_eof(self._response_eof)\n\n \n\ \ self.version = message.version\n self.status = message.code\n\ \ self.reason = message.reason\n\n \n self._headers = message.headers\ \ \n self._raw_headers = message.raw_headers \n\n \n self.content\ \ = payload\n\n \n for hdr in self.headers.getall(hdrs.SET_COOKIE,\ \ ()):\n try:\n self.cookies.load(hdr)\n \ \ except CookieError as exc:\n client_logger.warning(\n \ \ , exc)\n return self" - "def solve(self, verbose=False, allow_brute_force=True):\n \n while\ \ not self.is_solved:\n \n self._update()\n\n \ \ \n singles_found = False or self._fill_naked_singles() or self._fill_hidden_singles()\n\ \n \n \n \n if not singles_found:\n\ \ if allow_brute_force:\n solution = None\n\ \ try:\n dlxs = DancingLinksSolver(copy.deepcopy(self._matrix))\n\ \ solutions = dlxs.solve()\n solution\ \ = next(solutions)\n more_solutions = next(solutions)\n\ \ except StopIteration as e:\n if solution\ \ is not None:\n self._matrix = solution\n \ \ else:\n raise SudokuHasNoSolutionError(\"\ Dancing Links solver could not find any solution.\")\n except\ \ Exception as e:\n raise SudokuHasNoSolutionError(\"Brute\ \ Force method failed.\")\n else:\n \ \ \n \n raise SudokuHasMultipleSolutionsError(\"\ This Sudoku has multiple solutions!\")\n self.solution_steps.append(\"\ BRUTE FORCE - Dancing Links\")\n break\n else:\n\ \ print(self)\n raise SudokuTooDifficultError(\"\ This Sudoku requires more advanced methods!\")\n if verbose:\n \ \ print(\"Sudoku solved in {0} iterations!\\n{1}\".format(len(self.solution_steps),\ \ self))\n for step in self.solution_steps:\n print(step)" - "def get_peer_ips(self):\n \n presponse = [ord(i) for i in self.tracker_response[]]\n\ \ while presponse:\n peer_ip = ((.join(str(x) for x in presponse[0:4]),\n\ \ 256 * presponse[4] + presponse[5]))\n if peer_ip\ \ not in self.peer_ips:\n self.peer_ips.append(peer_ip)\n \ \ presponse = presponse[6:]" - source_sentence: "Setter method for ipv6_phy_intf_cmds, mapped from YANG variable\ \ /interface/fortygigabitethernet/ipv6/ipv6_phy_intf_cmds (container)\n If\ \ this variable is read-only (config: false) in the\n source YANG file, then\ \ _set_ipv6_phy_intf_cmds is considered as a private\n method. Backends looking\ \ to populate this variable should\n do so via calling thisObj._set_ipv6_phy_intf_cmds()\ \ directly." sentences: - "def _trim_xpath(self, xpath, prop):\n \n\n xroot = self._get_xroot_for(prop)\n\ \n if xroot is None and isinstance(xpath, string_types):\n xtags\ \ = xpath.split(XPATH_DELIM)\n\n if xtags[-1] in _iso_tag_primitives:\n\ \ xroot = XPATH_DELIM.join(xtags[:-1])\n\n return xroot" - "def _set_ipv6_phy_intf_cmds(self, v, load=False):\n \n if hasattr(v, \"\ _utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=ipv6_phy_intf_cmds.ipv6_phy_intf_cmds,\ \ is_container=, presence=False, yang_name=\"ipv6-phy-intf-cmds\", rest_name=\"\ \", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True,\ \ extensions={u: {u: u, u: None, u: u}}, namespace=, defining_module=, yang_type=,\ \ is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n\ \ : ,\n : \"container\",\n : ,\n })\n\n self.__ipv6_phy_intf_cmds\ \ = t\n if hasattr(self, ):\n self._set()" - "def create_snapshot(self, xml_bytes):\n \n root = XML(xml_bytes)\n\ \ snapshot_id = root.findtext(\"snapshotId\")\n volume_id = root.findtext(\"\ volumeId\")\n status = root.findtext(\"status\")\n start_time =\ \ root.findtext(\"startTime\")\n start_time = datetime.strptime(\n \ \ start_time[:19], \"%Y-%m-%dT%H:%M:%S\")\n progress = root.findtext(\"\ progress\")[:-1]\n progress = float(progress or \"0\") / 100.\n \ \ return model.Snapshot(\n snapshot_id, volume_id, status, start_time,\ \ progress)" - source_sentence: "Generates samples of text from the provided vocabulary.\n\n Args:\n\ \ plain_vocab: vocabulary.\n distribution: distribution.\n train_samples:\ \ samples for training.\n length: length.\n\n Returns:\n train_indices\ \ (np.array of Integers): random integers for training.\n shape = [num_samples,\ \ length]\n test_indices (np.array of Integers): random integers for testing.\n\ \ shape = [num_samples, length]\n plain_vocab (list of Integers): unique\ \ vocabularies." sentences: - "def late_filling(target, pressure=,\n Pc_star=,\n \ \ Swp_star=0.2, eta=3):\n r\n element = pressure.split()[0]\n network\ \ = target.project.network\n phase = target.project.find_phase(target)\n \ \ pc_star = phase[Pc_star]\n Pc = phase[pressure]\n \n Ts = network.map_throats(throats=target.Ts,\ \ origin=target)\n values = values[Ts]\n else:\n Ps = network.map_pores(pores=target.Ps,\ \ origin=target)\n values = values[Ps]\n return values" - "def switch(self, name):\n \n try:\n switch = self.storage[self.__namespaced(name)]\n\ \ except KeyError:\n if not self.autocreate:\n \ \ raise ValueError(\"No switch named registered in \" % (name, self.namespace))\n\ \n switch = self.__create_and_register_disabled_switch(name)\n\n \ \ switch.manager = self\n return switch" - "def generate_plaintext_random(plain_vocab, distribution, train_samples,\n \ \ length):\n \n if distribution is not None:\n \ \ assert len(distribution) == len(plain_vocab)\n\n train_indices = np.random.choice(\n\ \ range(len(plain_vocab)), (train_samples, length), p=distribution)\n\n \ \ return train_indices" pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@1 - cosine_ndcg@5 - cosine_ndcg@10 - cosine_mrr@1 - cosine_mrr@5 - cosine_mrr@10 - cosine_map@100 model-index: - name: SentenceTransformer based on Qwen/Qwen3-Embedding-0.6B results: - task: type: information-retrieval name: Information Retrieval dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy@1 value: 0.99 name: Cosine Accuracy@1 - type: cosine_accuracy@5 value: 1.0 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.99 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3333333333333334 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.19999999999999996 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09999999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.99 name: Cosine Recall@1 - type: cosine_recall@3 value: 1.0 name: Cosine Recall@3 - type: cosine_recall@5 value: 1.0 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@1 value: 0.99 name: Cosine Ndcg@1 - type: cosine_ndcg@5 value: 0.9963092975357145 name: Cosine Ndcg@5 - type: cosine_ndcg@10 value: 0.9963092975357145 name: Cosine Ndcg@10 - type: cosine_mrr@1 value: 0.99 name: Cosine Mrr@1 - type: cosine_mrr@5 value: 0.995 name: Cosine Mrr@5 - type: cosine_mrr@10 value: 0.995 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.995 name: Cosine Map@100 --- # 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). 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 ### 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, 'architecture': '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("JacobLinCool/Qwen3-Embedding-0.6B-GIR-1") # Run inference queries = [ "Generates samples of text from the provided vocabulary.\n\n Args:\n plain_vocab: vocabulary.\n distribution: distribution.\n train_samples: samples for training.\n length: length.\n\n Returns:\n train_indices (np.array of Integers): random integers for training.\n shape = [num_samples, length]\n test_indices (np.array of Integers): random integers for testing.\n shape = [num_samples, length]\n plain_vocab (list of Integers): unique vocabularies.", ] documents = [ 'def generate_plaintext_random(plain_vocab, distribution, train_samples,\n length):\n \n if distribution is not None:\n assert len(distribution) == len(plain_vocab)\n\n train_indices = np.random.choice(\n range(len(plain_vocab)), (train_samples, length), p=distribution)\n\n return train_indices', 'def switch(self, name):\n \n try:\n switch = self.storage[self.__namespaced(name)]\n except KeyError:\n if not self.autocreate:\n raise ValueError("No switch named registered in " % (name, self.namespace))\n\n switch = self.__create_and_register_disabled_switch(name)\n\n switch.manager = self\n return switch', 'def late_filling(target, pressure=,\n Pc_star=,\n Swp_star=0.2, eta=3):\n r\n element = pressure.split()[0]\n network = target.project.network\n phase = target.project.find_phase(target)\n pc_star = phase[Pc_star]\n Pc = phase[pressure]\n \n Ts = network.map_throats(throats=target.Ts, origin=target)\n values = values[Ts]\n else:\n Ps = network.map_pores(pores=target.Ps, origin=target)\n values = values[Ps]\n return values', ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) print(query_embeddings.shape, document_embeddings.shape) # [1, 1024] [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) # tensor([[ 0.8344, -0.0822, 0.0233]]) ``` ## Evaluation ### Metrics #### Information Retrieval * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.99 | | cosine_accuracy@5 | 1.0 | | cosine_accuracy@10 | 1.0 | | cosine_precision@1 | 0.99 | | cosine_precision@3 | 0.3333 | | cosine_precision@5 | 0.2 | | cosine_precision@10 | 0.1 | | cosine_recall@1 | 0.99 | | cosine_recall@3 | 1.0 | | cosine_recall@5 | 1.0 | | cosine_recall@10 | 1.0 | | cosine_ndcg@1 | 0.99 | | cosine_ndcg@5 | 0.9963 | | **cosine_ndcg@10** | **0.9963** | | cosine_mrr@1 | 0.99 | | cosine_mrr@5 | 0.995 | | cosine_mrr@10 | 0.995 | | cosine_map@100 | 0.995 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 400 training samples * Columns: query and code * Approximate statistics based on the first 400 samples: | | query | code | |:--------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | query | code | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | For memory actions, get a list of addresses it operates on.

:param SimAction action: The action object to work with.
:return: A list of addresses that are accessed with that action.
:rtype: list | def _get_actual_addrs(action, state):


if action.actual_addrs is None:

addr_list = {0x60000000}
else:
addr_list = set(action.actual_addrs)

return addr_list | | Construct the input file of the calculation. | def make_input(self, with_header=False):

s = str(self.input)
if with_header: s = str(self) + "\n" + s
return s | | Check worker status route | def check_worker_status():

if not in request.args:
resp = {"status": "bad request"}
return jsonify(**resp)
else:
worker_id = request.args[]
assignment_id = request.args[]
allow_repeats = CONFIG.getboolean(, )
if allow_repeats:
try:
part = Participant.query.\
filter(Participant.workerid == worker_id).\
filter(Participant.assignmentid == assignment_id).one()
status = part.status
except exc.SQLAlchemyError:
status = NOT_ACCEPTED
else:
try:
matches = Participant.query.\
filter(Participant.workerid == worker_id).all()
numrecs = len(matches)
if numrecs==0:
status = NOT_ACCEPTED
else:
status = max([record.status for record in matches])
except exc.SQLAlchemyError:
... | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim", "gather_across_devices": false } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 100 evaluation samples * Columns: query and code * Approximate statistics based on the first 100 samples: | | query | code | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | query | code | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Return the value of the android prefixed attribute in a specific tag.

This function will always try to get the attribute with a android: prefix first,
and will try to return the attribute without the prefix, if the attribute could not be found.
This is useful for some broken AndroidManifest.xml, where no android namespace is set,
but could also indicate malicious activity (i.e. wrongly repackaged files).
A warning is printed if the attribute is found without a namespace prefix.

If you require to get the exact result you need to query the tag directly:

example::
>>> from lxml.etree import Element
>>> tag = Element('bar', nsmap={'android': 'http://schemas.android.com/apk/res/android'})
>>> tag.set('{http://schemas.android.com/apk/res/android}foobar', 'barfoo')
>>> tag.set('name', 'baz')
# Assume that `a` is some APK object
>>> a.get_value_from_tag(tag, 'name'... | def get_value_from_tag(self, tag, attribute):




value = tag.get(self._ns(attribute))
if value is None:
value = tag.get(attribute)

if value:

log.warning("Failed to get the attribute on tag with namespace. "
"But found the same attribute without namespace!".format(attribute, tag.tag))
return value | | Get information about this object as a dictionary. Used by WebSocket interface to pass some
relevant information to client applications. | def get_as_datadict(self):

return dict(type=self.__class__.__name__, tags=list(self.tags)) | | Makes forecast with the estimated model

Parameters
----------
h : int (default : 5)
How many steps ahead would you like to forecast?

past_values : int (default : 20)
How many past observations to show on the forecast graph?

intervals : Boolean
Would you like to show 95% prediction intervals for the forecast?

Returns
----------
- Plot of the forecast | def plot_predict(self,h=5,past_values=20,intervals=True,**kwargs):

import matplotlib.pyplot as plt
import seaborn as sns

figsize = kwargs.get(,(10,7))

if self.latent_variables.estimated is False:
raise Exception("No latent variables estimated!")
else:

scale, shape, skewness = self._get_scale_and_shape(self.latent_variables.get_z_values(transformed=True))
previous_value = self.data[-1]
forecasted_values = np.ones(h)*self.states[-1]
date_index = self.shift_dates(h)
simulations = 10000
sim_vector = np.zeros([simulations,h])
t_params = self.transform_z()

for n in range(0,simulations):
rnd_q = np.random.normal(0,np.sqrt(self.latent_variables.get_z_values(transformed=True)[0]),h)
exp = forecasted_values.copy()

for t in range(0,h):
if t == 0:... | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim", "gather_across_devices": false } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `seed`: 2025 - `bf16`: True - `load_best_model_at_end`: True - `optim`: adamw_torch - `push_to_hub`: True - `hub_model_id`: JacobLinCool/Qwen3-Embedding-0.6B-GIR-1 - `hub_private_repo`: False - `gradient_checkpointing`: True - `eval_on_start`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `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`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `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`: 2025 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `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`: True - `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} - `parallelism_config`: 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`: True - `resume_from_checkpoint`: None - `hub_model_id`: JacobLinCool/Qwen3-Embedding-0.6B-GIR-1 - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `hub_revision`: None - `gradient_checkpointing`: True - `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`: True - `use_liger_kernel`: False - `liger_kernel_config`: None - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs | Epoch | Step | Validation Loss | cosine_ndcg@10 | |:-------:|:-----:|:---------------:|:--------------:| | 0 | 0 | 0.0616 | 0.9926 | | **1.0** | **7** | **0.0358** | **0.9963** | | -1 | -1 | - | 0.9963 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.11.11 - Sentence Transformers: 5.1.1 - Transformers: 4.56.2 - PyTorch: 2.8.0+cu128 - Accelerate: 1.10.1 - Datasets: 4.1.1 - Tokenizers: 0.22.1 ## 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", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```