JacobLinCool

This model has been pushed to the Hub using the PytorchModelHubMixin integration: - Library: https://github.com/yxlu-0102/MP-SENet - Docs: [More Information Needed]

You should use `Jacob` to trigger the image generation. Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc. Weights for this model are available in Safetensors format. For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers

SentenceTransformer based on Qwen/Qwen3-Embedding-8B This is a sentence-transformers model finetuned from Qwen/Qwen3-Embedding-8B. It maps sentences & paragraphs to a 4096-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. Model Description - Model Type: Sentence Transformer - Base model: Qwen/Qwen3-Embedding-8B - Maximum Sequence Length: 40960 tokens - Output Dimensionality: 4096 dimensions - Similarity Function: Cosine Similarity - Documentation: Sentence Transformers Documentation - Repository: Sentence Transformers on GitHub - Hugging Face: Sentence Transformers on Hugging Face | Metric | Value | |:--------------------|:-----------| | cosineaccuracy@1 | 0.99 | | cosineaccuracy@5 | 1.0 | | cosineaccuracy@10 | 1.0 | | cosineprecision@1 | 0.99 | | cosineprecision@3 | 0.3333 | | cosineprecision@5 | 0.2 | | cosineprecision@10 | 0.1 | | cosinerecall@1 | 0.99 | | cosinerecall@3 | 1.0 | | cosinerecall@5 | 1.0 | | cosinerecall@10 | 1.0 | | cosinendcg@1 | 0.99 | | cosinendcg@5 | 0.9963 | | cosinendcg@10 | 0.9963 | | cosinemrr@1 | 0.99 | | cosinemrr@5 | 0.995 | | cosinemrr@10 | 0.995 | | cosinemap@100 | 0.995 | Size: 400 training samples Columns: query and code Approximate statistics based on the first 400 samples: | | query | code | |:--------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------| | type | string | string | | details | min: 2 tokens mean: 67.12 tokens max: 3156 tokens | min: 24 tokens mean: 126.98 tokens max: 1236 tokens | 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 getactualaddrs(action, state): if action.actualaddrs is None: addrlist = {0x60000000} else: addrlist = set(action.actualaddrs) return addrlist | | Construct the input file of the calculation. | def makeinput(self, withheader=False): s = str(self.input) if withheader: s = str(self) + "\n" + s return s | | Check worker status route | def checkworkerstatus(): if not in request.args: resp = {"status": "bad request"} return jsonify(resp) else: workerid = request.args[] assignmentid = request.args[] allowrepeats = CONFIG.getboolean(, ) if allowrepeats: try: part = Participant.query.\ filter(Participant.workerid == workerid).\ filter(Participant.assignmentid == assignmentid).one() status = part.status except exc.SQLAlchemyError: status = NOTACCEPTED else: try: matches = Participant.query.\ filter(Participant.workerid == workerid).all() numrecs = len(matches) if numrecs==0: status = NOTACCEPTED else: status = max([record.status for record in matches]) except exc.SQLAlchemyError: ... | Loss: CachedMultipleNegativesRankingLoss with these parameters: Size: 100 evaluation samples Columns: query and code Approximate statistics based on the first 100 samples: | | query | code | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | min: 5 tokens mean: 66.56 tokens max: 548 tokens | min: 24 tokens mean: 142.11 tokens max: 901 tokens | 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.getvaluefromtag(tag, 'name'... | def getvaluefromtag(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 getasdatadict(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? pastvalues : 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 plotpredict(self,h=5,pastvalues=20,intervals=True,kwargs): import matplotlib.pyplot as plt import seaborn as sns figsize = kwargs.get(,(10,7)) if self.latentvariables.estimated is False: raise Exception("No latent variables estimated!") else: scale, shape, skewness = self.getscaleandshape(self.latentvariables.getzvalues(transformed=True)) previousvalue = self.data[-1] forecastedvalues = np.ones(h)self.states[-1] dateindex = self.shiftdates(h) simulations = 10000 simvector = np.zeros([simulations,h]) tparams = self.transformz() for n in range(0,simulations): rndq = np.random.normal(0,np.sqrt(self.latentvariables.getzvalues(transformed=True)[0]),h) exp = forecastedvalues.copy() for t in range(0,h): if t == 0:... | Loss: CachedMultipleNegativesRankingLoss with these parameters: Training Hyperparameters Non-Default Hyperparameters - `evalstrategy`: epoch - `perdevicetrainbatchsize`: 64 - `perdeviceevalbatchsize`: 64 - `learningrate`: 2e-05 - `numtrainepochs`: 5 - `warmupratio`: 0.1 - `seed`: 2025 - `bf16`: True - `loadbestmodelatend`: True - `optim`: pagedadamw8bit - `pushtohub`: True - `hubmodelid`: JacobLinCool/Qwen3-Embedding-8B-GIR-1 - `hubprivaterepo`: False - `gradientcheckpointing`: True - `evalonstart`: True - `batchsampler`: noduplicates - `overwriteoutputdir`: False - `dopredict`: False - `evalstrategy`: epoch - `predictionlossonly`: True - `perdevicetrainbatchsize`: 64 - `perdeviceevalbatchsize`: 64 - `pergputrainbatchsize`: None - `pergpuevalbatchsize`: None - `gradientaccumulationsteps`: 1 - `evalaccumulationsteps`: None - `torchemptycachesteps`: None - `learningrate`: 2e-05 - `weightdecay`: 0.0 - `adambeta1`: 0.9 - `adambeta2`: 0.999 - `adamepsilon`: 1e-08 - `maxgradnorm`: 1.0 - `numtrainepochs`: 5 - `maxsteps`: -1 - `lrschedulertype`: linear - `lrschedulerkwargs`: {} - `warmupratio`: 0.1 - `warmupsteps`: 0 - `loglevel`: passive - `loglevelreplica`: warning - `logoneachnode`: True - `loggingnaninffilter`: True - `savesafetensors`: True - `saveoneachnode`: False - `saveonlymodel`: False - `restorecallbackstatesfromcheckpoint`: False - `nocuda`: False - `usecpu`: False - `usempsdevice`: False - `seed`: 2025 - `dataseed`: None - `jitmodeeval`: False - `useipex`: False - `bf16`: True - `fp16`: False - `fp16optlevel`: O1 - `halfprecisionbackend`: auto - `bf16fulleval`: False - `fp16fulleval`: False - `tf32`: None - `localrank`: 0 - `ddpbackend`: None - `tpunumcores`: None - `tpumetricsdebug`: False - `debug`: [] - `dataloaderdroplast`: False - `dataloadernumworkers`: 0 - `dataloaderprefetchfactor`: None - `pastindex`: -1 - `disabletqdm`: False - `removeunusedcolumns`: True - `labelnames`: None - `loadbestmodelatend`: True - `ignoredataskip`: False - `fsdp`: [] - `fsdpminnumparams`: 0 - `fsdpconfig`: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgradckpt': False} - `fsdptransformerlayerclstowrap`: None - `acceleratorconfig`: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None} - `parallelismconfig`: None - `deepspeed`: None - `labelsmoothingfactor`: 0.0 - `optim`: pagedadamw8bit - `optimargs`: None - `adafactor`: False - `groupbylength`: False - `lengthcolumnname`: length - `ddpfindunusedparameters`: None - `ddpbucketcapmb`: None - `ddpbroadcastbuffers`: False - `dataloaderpinmemory`: True - `dataloaderpersistentworkers`: False - `skipmemorymetrics`: True - `uselegacypredictionloop`: False - `pushtohub`: True - `resumefromcheckpoint`: None - `hubmodelid`: JacobLinCool/Qwen3-Embedding-8B-GIR-1 - `hubstrategy`: everysave - `hubprivaterepo`: False - `hubalwayspush`: False - `hubrevision`: None - `gradientcheckpointing`: True - `gradientcheckpointingkwargs`: None - `includeinputsformetrics`: False - `includeformetrics`: [] - `evaldoconcatbatches`: True - `fp16backend`: auto - `pushtohubmodelid`: None - `pushtohuborganization`: None - `mpparameters`: - `autofindbatchsize`: False - `fulldeterminism`: False - `torchdynamo`: None - `rayscope`: last - `ddptimeout`: 1800 - `torchcompile`: False - `torchcompilebackend`: None - `torchcompilemode`: None - `includetokenspersecond`: False - `includenuminputtokensseen`: False - `neftunenoisealpha`: None - `optimtargetmodules`: None - `batchevalmetrics`: False - `evalonstart`: True - `useligerkernel`: False - `ligerkernelconfig`: None - `evalusegatherobject`: False - `averagetokensacrossdevices`: False - `prompts`: None - `batchsampler`: noduplicates - `multidatasetbatchsampler`: proportional - `routermapping`: {} - `learningratemapping`: {} Training Logs | Epoch | Step | Validation Loss | cosinendcg@10 | |:-------:|:------:|:---------------:|:--------------:| | 0 | 0 | 0.0559 | 0.9926 | | 1.0 | 7 | 0.0370 | 0.9963 | | 2.0 | 14 | 0.0319 | 0.9963 | | 3.0 | 21 | 0.0312 | 0.9963 | | 4.0 | 28 | 0.0312 | 0.9963 | | 5.0 | 35 | 0.0313 | 0.9963 | | -1 | -1 | - | 0.9963 | 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

This model is a fine-tuned version of google/gemma-3n-E4B-it. It has been trained using TRL. - PEFT 0.15.2 - TRL: 0.19.0 - Transformers: 4.53.0 - Pytorch: 2.8.0.dev20250319+cu128 - Datasets: 3.6.0 - Tokenizers: 0.21.2

This model is a fine-tuned version of openai/whisper-large-v3-turbo on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3507 - Wer: 8.4297 - Cer: 4.3573 The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 8 - evalbatchsize: 32 - seed: 42 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 32 - optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments - lrschedulertype: linear - lrschedulerwarmupsteps: 500 - trainingsteps: 5000 | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:------:|:----:|:---------------:|:-------:|:-------:| | No log | 0 | 0 | 2.8591 | 23.2283 | 14.9979 | | 0.3457 | 0.1 | 500 | 0.2787 | 9.8557 | 4.9508 | | 0.252 | 1.0748 | 1000 | 0.2785 | 10.9926 | 5.6876 | | 0.1053 | 2.0496 | 1500 | 0.2708 | 9.1643 | 4.5877 | | 0.0505 | 3.0244 | 2000 | 0.3046 | 9.9821 | 5.4330 | | 0.0544 | 3.1244 | 2500 | 0.2819 | 8.8718 | 4.4522 | | 0.0209 | 4.0992 | 3000 | 0.3062 | 9.5699 | 5.1405 | | 0.0111 | 5.074 | 3500 | 0.3224 | 8.5394 | 4.4230 | | 0.0023 | 6.0488 | 4000 | 0.3427 | 8.4131 | 4.3766 | | 0.0018 | 7.0236 | 4500 | 0.3489 | 8.3932 | 4.3516 | | 0.0018 | 7.1236 | 5000 | 0.3507 | 8.4297 | 4.3573 | - Transformers 4.54.0 - Pytorch 2.8.0.dev20250319+cu128 - Datasets 3.6.0 - Tokenizers 0.21.2

SentenceTransformer based on Qwen/Qwen3-Embedding-0.6B This is a sentence-transformers model finetuned from 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 Description - Model Type: Sentence Transformer - Base model: Qwen/Qwen3-Embedding-0.6B - Maximum Sequence Length: 32768 tokens - Output Dimensionality: 1024 dimensions - Similarity Function: Cosine Similarity - Documentation: Sentence Transformers Documentation - Repository: Sentence Transformers on GitHub - Hugging Face: Sentence Transformers on Hugging Face | Metric | Value | |:--------------------|:-----------| | cosineaccuracy@1 | 0.99 | | cosineaccuracy@5 | 1.0 | | cosineaccuracy@10 | 1.0 | | cosineprecision@1 | 0.99 | | cosineprecision@3 | 0.3333 | | cosineprecision@5 | 0.2 | | cosineprecision@10 | 0.1 | | cosinerecall@1 | 0.99 | | cosinerecall@3 | 1.0 | | cosinerecall@5 | 1.0 | | cosinerecall@10 | 1.0 | | cosinendcg@1 | 0.99 | | cosinendcg@5 | 0.9963 | | cosinendcg@10 | 0.9963 | | cosinemrr@1 | 0.99 | | cosinemrr@5 | 0.995 | | cosinemrr@10 | 0.995 | | cosinemap@100 | 0.995 | Size: 400 training samples Columns: query and code Approximate statistics based on the first 400 samples: | | query | code | |:--------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------| | type | string | string | | details | min: 2 tokens mean: 67.12 tokens max: 3156 tokens | min: 24 tokens mean: 126.98 tokens max: 1236 tokens | 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 getactualaddrs(action, state): if action.actualaddrs is None: addrlist = {0x60000000} else: addrlist = set(action.actualaddrs) return addrlist | | Construct the input file of the calculation. | def makeinput(self, withheader=False): s = str(self.input) if withheader: s = str(self) + "\n" + s return s | | Check worker status route | def checkworkerstatus(): if not in request.args: resp = {"status": "bad request"} return jsonify(resp) else: workerid = request.args[] assignmentid = request.args[] allowrepeats = CONFIG.getboolean(, ) if allowrepeats: try: part = Participant.query.\ filter(Participant.workerid == workerid).\ filter(Participant.assignmentid == assignmentid).one() status = part.status except exc.SQLAlchemyError: status = NOTACCEPTED else: try: matches = Participant.query.\ filter(Participant.workerid == workerid).all() numrecs = len(matches) if numrecs==0: status = NOTACCEPTED else: status = max([record.status for record in matches]) except exc.SQLAlchemyError: ... | Loss: MultipleNegativesRankingLoss with these parameters: Size: 100 evaluation samples Columns: query and code Approximate statistics based on the first 100 samples: | | query | code | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | min: 5 tokens mean: 66.56 tokens max: 548 tokens | min: 24 tokens mean: 142.11 tokens max: 901 tokens | 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.getvaluefromtag(tag, 'name'... | def getvaluefromtag(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 getasdatadict(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? pastvalues : 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 plotpredict(self,h=5,pastvalues=20,intervals=True,kwargs): import matplotlib.pyplot as plt import seaborn as sns figsize = kwargs.get(,(10,7)) if self.latentvariables.estimated is False: raise Exception("No latent variables estimated!") else: scale, shape, skewness = self.getscaleandshape(self.latentvariables.getzvalues(transformed=True)) previousvalue = self.data[-1] forecastedvalues = np.ones(h)self.states[-1] dateindex = self.shiftdates(h) simulations = 10000 simvector = np.zeros([simulations,h]) tparams = self.transformz() for n in range(0,simulations): rndq = np.random.normal(0,np.sqrt(self.latentvariables.getzvalues(transformed=True)[0]),h) exp = forecastedvalues.copy() for t in range(0,h): if t == 0:... | Loss: MultipleNegativesRankingLoss with these parameters: Training Hyperparameters Non-Default Hyperparameters - `evalstrategy`: epoch - `perdevicetrainbatchsize`: 64 - `perdeviceevalbatchsize`: 64 - `numtrainepochs`: 1 - `warmupratio`: 0.1 - `seed`: 2025 - `bf16`: True - `loadbestmodelatend`: True - `optim`: adamwtorch - `pushtohub`: True - `hubmodelid`: JacobLinCool/Qwen3-Embedding-0.6B-GIR-1 - `hubprivaterepo`: False - `gradientcheckpointing`: True - `evalonstart`: True - `batchsampler`: noduplicates - `overwriteoutputdir`: False - `dopredict`: False - `evalstrategy`: epoch - `predictionlossonly`: True - `perdevicetrainbatchsize`: 64 - `perdeviceevalbatchsize`: 64 - `pergputrainbatchsize`: None - `pergpuevalbatchsize`: None - `gradientaccumulationsteps`: 1 - `evalaccumulationsteps`: None - `torchemptycachesteps`: None - `learningrate`: 5e-05 - `weightdecay`: 0.0 - `adambeta1`: 0.9 - `adambeta2`: 0.999 - `adamepsilon`: 1e-08 - `maxgradnorm`: 1.0 - `numtrainepochs`: 1 - `maxsteps`: -1 - `lrschedulertype`: linear - `lrschedulerkwargs`: {} - `warmupratio`: 0.1 - `warmupsteps`: 0 - `loglevel`: passive - `loglevelreplica`: warning - `logoneachnode`: True - `loggingnaninffilter`: True - `savesafetensors`: True - `saveoneachnode`: False - `saveonlymodel`: False - `restorecallbackstatesfromcheckpoint`: False - `nocuda`: False - `usecpu`: False - `usempsdevice`: False - `seed`: 2025 - `dataseed`: None - `jitmodeeval`: False - `useipex`: False - `bf16`: True - `fp16`: False - `fp16optlevel`: O1 - `halfprecisionbackend`: auto - `bf16fulleval`: False - `fp16fulleval`: False - `tf32`: None - `localrank`: 0 - `ddpbackend`: None - `tpunumcores`: None - `tpumetricsdebug`: False - `debug`: [] - `dataloaderdroplast`: False - `dataloadernumworkers`: 0 - `dataloaderprefetchfactor`: None - `pastindex`: -1 - `disabletqdm`: False - `removeunusedcolumns`: True - `labelnames`: None - `loadbestmodelatend`: True - `ignoredataskip`: False - `fsdp`: [] - `fsdpminnumparams`: 0 - `fsdpconfig`: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgradckpt': False} - `fsdptransformerlayerclstowrap`: None - `acceleratorconfig`: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None} - `parallelismconfig`: None - `deepspeed`: None - `labelsmoothingfactor`: 0.0 - `optim`: adamwtorch - `optimargs`: None - `adafactor`: False - `groupbylength`: False - `lengthcolumnname`: length - `ddpfindunusedparameters`: None - `ddpbucketcapmb`: None - `ddpbroadcastbuffers`: False - `dataloaderpinmemory`: True - `dataloaderpersistentworkers`: False - `skipmemorymetrics`: True - `uselegacypredictionloop`: False - `pushtohub`: True - `resumefromcheckpoint`: None - `hubmodelid`: JacobLinCool/Qwen3-Embedding-0.6B-GIR-1 - `hubstrategy`: everysave - `hubprivaterepo`: False - `hubalwayspush`: False - `hubrevision`: None - `gradientcheckpointing`: True - `gradientcheckpointingkwargs`: None - `includeinputsformetrics`: False - `includeformetrics`: [] - `evaldoconcatbatches`: True - `fp16backend`: auto - `pushtohubmodelid`: None - `pushtohuborganization`: None - `mpparameters`: - `autofindbatchsize`: False - `fulldeterminism`: False - `torchdynamo`: None - `rayscope`: last - `ddptimeout`: 1800 - `torchcompile`: False - `torchcompilebackend`: None - `torchcompilemode`: None - `includetokenspersecond`: False - `includenuminputtokensseen`: False - `neftunenoisealpha`: None - `optimtargetmodules`: None - `batchevalmetrics`: False - `evalonstart`: True - `useligerkernel`: False - `ligerkernelconfig`: None - `evalusegatherobject`: False - `averagetokensacrossdevices`: False - `prompts`: None - `batchsampler`: noduplicates - `multidatasetbatchsampler`: proportional - `routermapping`: {} - `learningratemapping`: {} Training Logs | Epoch | Step | Validation Loss | cosinendcg@10 | |:-------:|:-----:|:---------------:|:--------------:| | 0 | 0 | 0.0616 | 0.9926 | | 1.0 | 7 | 0.0358 | 0.9963 | | -1 | -1 | - | 0.9963 | 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

SentenceTransformer based on Qwen/Qwen3-Embedding-4B This is a sentence-transformers model finetuned from Qwen/Qwen3-Embedding-4B. It maps sentences & paragraphs to a 2560-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. Model Description - Model Type: Sentence Transformer - Base model: Qwen/Qwen3-Embedding-4B - Maximum Sequence Length: 40960 tokens - Output Dimensionality: 2560 dimensions - Similarity Function: Cosine Similarity - Documentation: Sentence Transformers Documentation - Repository: Sentence Transformers on GitHub - Hugging Face: Sentence Transformers on Hugging Face | Metric | Value | |:--------------------|:-----------| | cosineaccuracy@1 | 0.98 | | cosineaccuracy@5 | 1.0 | | cosineaccuracy@10 | 1.0 | | cosineprecision@1 | 0.98 | | cosineprecision@3 | 0.3333 | | cosineprecision@5 | 0.2 | | cosineprecision@10 | 0.1 | | cosinerecall@1 | 0.98 | | cosinerecall@3 | 1.0 | | cosinerecall@5 | 1.0 | | cosinerecall@10 | 1.0 | | cosinendcg@1 | 0.98 | | cosinendcg@5 | 0.9926 | | cosinendcg@10 | 0.9926 | | cosinemrr@1 | 0.98 | | cosinemrr@5 | 0.99 | | cosinemrr@10 | 0.99 | | cosinemap@100 | 0.99 | Size: 400 training samples Columns: query and code Approximate statistics based on the first 400 samples: | | query | code | |:--------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------| | type | string | string | | details | min: 2 tokens mean: 67.12 tokens max: 3156 tokens | min: 24 tokens mean: 126.98 tokens max: 1236 tokens | 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 getactualaddrs(action, state): if action.actualaddrs is None: addrlist = {0x60000000} else: addrlist = set(action.actualaddrs) return addrlist | | Construct the input file of the calculation. | def makeinput(self, withheader=False): s = str(self.input) if withheader: s = str(self) + "\n" + s return s | | Check worker status route | def checkworkerstatus(): if not in request.args: resp = {"status": "bad request"} return jsonify(resp) else: workerid = request.args[] assignmentid = request.args[] allowrepeats = CONFIG.getboolean(, ) if allowrepeats: try: part = Participant.query.\ filter(Participant.workerid == workerid).\ filter(Participant.assignmentid == assignmentid).one() status = part.status except exc.SQLAlchemyError: status = NOTACCEPTED else: try: matches = Participant.query.\ filter(Participant.workerid == workerid).all() numrecs = len(matches) if numrecs==0: status = NOTACCEPTED else: status = max([record.status for record in matches]) except exc.SQLAlchemyError: ... | Loss: CachedMultipleNegativesRankingLoss with these parameters: Size: 100 evaluation samples Columns: query and code Approximate statistics based on the first 100 samples: | | query | code | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | min: 5 tokens mean: 66.56 tokens max: 548 tokens | min: 24 tokens mean: 142.11 tokens max: 901 tokens | 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.getvaluefromtag(tag, 'name'... | def getvaluefromtag(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 getasdatadict(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? pastvalues : 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 plotpredict(self,h=5,pastvalues=20,intervals=True,kwargs): import matplotlib.pyplot as plt import seaborn as sns figsize = kwargs.get(,(10,7)) if self.latentvariables.estimated is False: raise Exception("No latent variables estimated!") else: scale, shape, skewness = self.getscaleandshape(self.latentvariables.getzvalues(transformed=True)) previousvalue = self.data[-1] forecastedvalues = np.ones(h)self.states[-1] dateindex = self.shiftdates(h) simulations = 10000 simvector = np.zeros([simulations,h]) tparams = self.transformz() for n in range(0,simulations): rndq = np.random.normal(0,np.sqrt(self.latentvariables.getzvalues(transformed=True)[0]),h) exp = forecastedvalues.copy() for t in range(0,h): if t == 0:... | Loss: CachedMultipleNegativesRankingLoss with these parameters: Training Hyperparameters Non-Default Hyperparameters - `evalstrategy`: epoch - `perdevicetrainbatchsize`: 64 - `perdeviceevalbatchsize`: 64 - `numtrainepochs`: 1 - `warmupratio`: 0.1 - `seed`: 2025 - `bf16`: True - `loadbestmodelatend`: True - `optim`: adamwtorch - `pushtohub`: True - `hubmodelid`: JacobLinCool/Qwen3-Embedding-4B-GIR-1 - `hubprivaterepo`: False - `gradientcheckpointing`: True - `evalonstart`: True - `batchsampler`: noduplicates - `overwriteoutputdir`: False - `dopredict`: False - `evalstrategy`: epoch - `predictionlossonly`: True - `perdevicetrainbatchsize`: 64 - `perdeviceevalbatchsize`: 64 - `pergputrainbatchsize`: None - `pergpuevalbatchsize`: None - `gradientaccumulationsteps`: 1 - `evalaccumulationsteps`: None - `torchemptycachesteps`: None - `learningrate`: 5e-05 - `weightdecay`: 0.0 - `adambeta1`: 0.9 - `adambeta2`: 0.999 - `adamepsilon`: 1e-08 - `maxgradnorm`: 1.0 - `numtrainepochs`: 1 - `maxsteps`: -1 - `lrschedulertype`: linear - `lrschedulerkwargs`: {} - `warmupratio`: 0.1 - `warmupsteps`: 0 - `loglevel`: passive - `loglevelreplica`: warning - `logoneachnode`: True - `loggingnaninffilter`: True - `savesafetensors`: True - `saveoneachnode`: False - `saveonlymodel`: False - `restorecallbackstatesfromcheckpoint`: False - `nocuda`: False - `usecpu`: False - `usempsdevice`: False - `seed`: 2025 - `dataseed`: None - `jitmodeeval`: False - `useipex`: False - `bf16`: True - `fp16`: False - `fp16optlevel`: O1 - `halfprecisionbackend`: auto - `bf16fulleval`: False - `fp16fulleval`: False - `tf32`: None - `localrank`: 0 - `ddpbackend`: None - `tpunumcores`: None - `tpumetricsdebug`: False - `debug`: [] - `dataloaderdroplast`: False - `dataloadernumworkers`: 0 - `dataloaderprefetchfactor`: None - `pastindex`: -1 - `disabletqdm`: False - `removeunusedcolumns`: True - `labelnames`: None - `loadbestmodelatend`: True - `ignoredataskip`: False - `fsdp`: [] - `fsdpminnumparams`: 0 - `fsdpconfig`: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgradckpt': False} - `fsdptransformerlayerclstowrap`: None - `acceleratorconfig`: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None} - `parallelismconfig`: None - `deepspeed`: None - `labelsmoothingfactor`: 0.0 - `optim`: adamwtorch - `optimargs`: None - `adafactor`: False - `groupbylength`: False - `lengthcolumnname`: length - `ddpfindunusedparameters`: None - `ddpbucketcapmb`: None - `ddpbroadcastbuffers`: False - `dataloaderpinmemory`: True - `dataloaderpersistentworkers`: False - `skipmemorymetrics`: True - `uselegacypredictionloop`: False - `pushtohub`: True - `resumefromcheckpoint`: None - `hubmodelid`: JacobLinCool/Qwen3-Embedding-4B-GIR-1 - `hubstrategy`: everysave - `hubprivaterepo`: False - `hubalwayspush`: False - `hubrevision`: None - `gradientcheckpointing`: True - `gradientcheckpointingkwargs`: None - `includeinputsformetrics`: False - `includeformetrics`: [] - `evaldoconcatbatches`: True - `fp16backend`: auto - `pushtohubmodelid`: None - `pushtohuborganization`: None - `mpparameters`: - `autofindbatchsize`: False - `fulldeterminism`: False - `torchdynamo`: None - `rayscope`: last - `ddptimeout`: 1800 - `torchcompile`: False - `torchcompilebackend`: None - `torchcompilemode`: None - `includetokenspersecond`: False - `includenuminputtokensseen`: False - `neftunenoisealpha`: None - `optimtargetmodules`: None - `batchevalmetrics`: False - `evalonstart`: True - `useligerkernel`: False - `ligerkernelconfig`: None - `evalusegatherobject`: False - `averagetokensacrossdevices`: False - `prompts`: None - `batchsampler`: noduplicates - `multidatasetbatchsampler`: proportional - `routermapping`: {} - `learningratemapping`: {} Training Logs | Epoch | Step | Validation Loss | cosinendcg@10 | |:-------:|:-----:|:---------------:|:--------------:| | 0 | 0 | 0.0663 | 0.9889 | | 1.0 | 7 | 0.0594 | 0.9926 | 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

This model is a fine-tuned version of openai/whisper-large-v3-turbo on the commonvoice161 dataset. It achieves the following results on the evaluation set: - Loss: 1.2775 - Wer: 60.3430 The following hyperparameters were used during training: - learningrate: 1e-05 - trainbatchsize: 16 - evalbatchsize: 32 - seed: 42 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 32 - optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments - lrschedulertype: linear - lrschedulerwarmupsteps: 500 - trainingsteps: 5000 | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0 | 0 | 7.6611 | 130.4102 | | 1.3625 | 0.1 | 500 | 2.0087 | 73.6922 | | 0.9901 | 0.2 | 1000 | 1.4311 | 58.1072 | | 0.7534 | 0.3 | 1500 | 1.3291 | 55.8715 | | 0.8052 | 0.4 | 2000 | 1.3063 | 50.5318 | | 0.7024 | 0.5 | 2500 | 1.2896 | 46.6464 | | 0.926 | 0.6 | 3000 | 1.2833 | 59.4747 | | 0.6204 | 0.7 | 3500 | 1.2788 | 49.9674 | | 0.7267 | 0.8 | 4000 | 1.2791 | 58.9755 | | 0.8442 | 0.9 | 4500 | 1.2775 | 50.2930 | | 0.9145 | 1.0 | 5000 | 1.2775 | 60.3430 | - Transformers 4.54.0 - Pytorch 2.8.0.dev20250319+cu128 - Datasets 3.6.0 - Tokenizers 0.21.2

This model is a fine-tuned version of openai/whisper-large-v3-turbo on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2787 - Wer: 8.5361 - Cer: 4.3966 The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 8 - evalbatchsize: 32 - seed: 42 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 32 - optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments - lrschedulertype: linear - lrschedulerwarmupsteps: 500 - trainingsteps: 5000 | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:------:|:----:|:---------------:|:-------:|:-------:| | No log | 0 | 0 | 2.8591 | 23.2283 | 14.9979 | | 0.3071 | 0.1 | 500 | 0.2623 | 9.2208 | 4.7068 | | 0.2188 | 1.0748 | 1000 | 0.2464 | 9.7893 | 5.1491 | | 0.1338 | 2.0496 | 1500 | 0.2448 | 8.4231 | 4.2760 | | 0.0891 | 3.0244 | 2000 | 0.2530 | 9.1776 | 4.8046 | | 0.0864 | 3.1244 | 2500 | 0.2500 | 8.5760 | 4.3873 | | 0.0584 | 4.0992 | 3000 | 0.2635 | 8.4729 | 4.3588 | | 0.0344 | 5.074 | 3500 | 0.2707 | 8.4829 | 4.3645 | | 0.0269 | 6.0488 | 4000 | 0.2763 | 8.5461 | 4.3987 | | 0.0224 | 7.0236 | 4500 | 0.2787 | 8.5295 | 4.4058 | | 0.025 | 7.1236 | 5000 | 0.2787 | 8.5361 | 4.3966 | - Transformers 4.54.0 - Pytorch 2.8.0.dev20250319+cu128 - Datasets 3.6.0 - Tokenizers 0.21.4

This model has been pushed to the Hub using the PytorchModelHubMixin integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]

This model has been pushed to the Hub using the PytorchModelHubMixin integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]

This model has been pushed to the Hub using the PytorchModelHubMixin integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]