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README.md
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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---
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# {
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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## Training
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The
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**DataLoader**:
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`torch.utils.data.dataloader.DataLoader` of length 43680 with parameters:
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```
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{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
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```
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**Loss**:
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`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
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```
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{'scale': 20.0, 'similarity_fct': 'cos_sim'}
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```
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Parameters of the fit()-Method:
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```
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{
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"epochs": 2,
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"evaluation_steps": 0,
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"evaluator": "NoneType",
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"max_grad_norm": 1,
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"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
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"optimizer_params": {
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"lr": 2e-05
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},
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"scheduler": "WarmupLinear",
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"steps_per_epoch": null,
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"warmup_steps": 8736,
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"weight_decay": 0.01
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}
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```
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## Full Model Architecture
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(2): Normalize()
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```
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## Citing & Authors
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<!--- Describe where people can find more information -->
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- transformers
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---
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# {all-mpnet-base-v2-eclass}
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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## Training
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The Model was trained with the eclass-dataset (https://huggingface.co/datasets/JoBeer/eclassTrainST).
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## Full Model Architecture
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(2): Normalize()
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)
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```
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