aari1995

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	---
	pipeline_tag: sentence-similarity
	language:
	- de
	datasets:
	- stsb_multi_mt
	tags:
	- gBERT-large
	- sentence-transformers
	- feature-extraction
	- sentence-similarity
	- transformers
	- RAG
	- retrieval augmented generation
	- STS
	- MTEB
	---

	# German_Semantic_STS_V2

	This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.

	Special thanks to [deepset](https://huggingface.co/deepset/) for providing the model gBERT-large and also to [Philip May](https://huggingface.co/philipMay) for the Translation of the dataset and chats about the topic.

	Model score after fine-tuning scores best, compared to these models:

	\| Model Name \| Spearman \|
	\|---------------------------------------------------------------\|-------------------\|
	\| xlm-r-distilroberta-base-paraphrase-v1 \| 0.8079 \|
	\| [xlm-r-100langs-bert-base-nli-stsb-mean-tokens](https://huggingface.co/sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens) \| 0.7877 \|
	\| xlm-r-bert-base-nli-stsb-mean-tokens \| 0.7877 \|
	\| [roberta-large-nli-stsb-mean-tokens](https://huggingface.co/sentence-transformers/roberta-large-nli-stsb-mean-tokens) \| 0.6371 \|
	\| [T-Systems-onsite/<br/>german-roberta-sentence-transformer-v2](https://huggingface.co/T-Systems-onsite/german-roberta-sentence-transformer-v2) \| 0.8529 \|
	\| [paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) \| 0.8355 \|
	\| [T-Systems-onsite/<br/>cross-en-de-roberta-sentence-transformer](https://huggingface.co/T-Systems-onsite/<br/>cross-en-de-roberta-sentence-transformer) \| 0.8550 \|
	\| aari1995/German_Semantic_STS_V2 \| 0.8626 \|



	<!--- Describe your model here -->

	## Usage (Sentence-Transformers)

	Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

	```
	pip install -U sentence-transformers
	```

	Then you can use the model like this:

	```python
	from sentence_transformers import SentenceTransformer
	sentences = ["This is an example sentence", "Each sentence is converted"]

	model = SentenceTransformer('aari1995/German_Semantic_STS_V2')
	embeddings = model.encode(sentences)
	print(embeddings)
	```



	## Usage (HuggingFace Transformers)
	Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

	```python
	from transformers import AutoTokenizer, AutoModel
	import torch


	#Mean Pooling - Take attention mask into account for correct averaging
	def mean_pooling(model_output, attention_mask):
	token_embeddings = model_output[0] #First element of model_output contains all token embeddings
	input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
	return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


	# Sentences we want sentence embeddings for
	sentences = ['This is an example sentence', 'Each sentence is converted']

	# Load model from HuggingFace Hub
	tokenizer = AutoTokenizer.from_pretrained('aari1995/German_Semantic_STS_V2')
	model = AutoModel.from_pretrained('aari1995/German_Semantic_STS_V2')

	# Tokenize sentences
	encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

	# Compute token embeddings
	with torch.no_grad():
	model_output = model(**encoded_input)

	# Perform pooling. In this case, mean pooling.
	sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

	print("Sentence embeddings:")
	print(sentence_embeddings)
	```



	## Evaluation Results

	<!--- Describe how your model was evaluated -->

	For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})


	## Training
	The model was trained with the parameters:

	DataLoader:

	`torch.utils.data.dataloader.DataLoader` of length 1438 with parameters:
	```
	{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
	```

	Loss:

	`sentence_transformers.losses.ContrastiveLoss.ContrastiveLoss` with parameters:
	```
	{'distance_metric': 'SiameseDistanceMetric.COSINE_DISTANCE', 'margin': 0.5, 'size_average': True}
	```

	Parameters of the fit()-Method:
	```
	{
	"epochs": 4,
	"evaluation_steps": 500,
	"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
	"max_grad_norm": 1,
	"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
	"optimizer_params": {
	"lr": 5e-06
	},
	"scheduler": "WarmupLinear",
	"steps_per_epoch": null,
	"warmup_steps": 576,
	"weight_decay": 0.01
	}
	```


	## Full Model Architecture
	```
	SentenceTransformer(
	(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
	(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
	)
	```

	## Citing & Authors

	<!--- Describe where people can find more information -->
	The base model is trained by deepset.
	The dataset was published / translated by Philip May.
	The model was fine-tuned by Aaron Chibb.