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
• mteb

model-index:

• name: German_Semantic_STS_V2 results:
  • dataset: config: de name: MTEB AmazonCounterfactualClassification revision: e8379541af4e31359cca9fbcf4b00f2671dba205 split: test type: mteb/amazon_counterfactual metrics:
    • type: accuracy value: 67.00214132762312 task: type: Classification
  • dataset: config: de name: MTEB AmazonCounterfactualClassification revision: e8379541af4e31359cca9fbcf4b00f2671dba205 split: validation type: mteb/amazon_counterfactual metrics:
    • type: accuracy value: 68.43347639484978 task: type: Classification
  • dataset: config: de name: MTEB AmazonReviewsClassification revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics:
    • type: accuracy value: 39.092 task: type: Classification
  • dataset: config: de name: MTEB AmazonReviewsClassification revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: validation type: mteb/amazon_reviews_multi metrics:
    • type: accuracy value: 39.146000000000003 task: type: Classification
  • dataset: config: default name: MTEB BlurbsClusteringP2P revision: a2dd5b02a77de3466a3eaa98ae586b5610314496 split: test type: slvnwhrl/blurbs-clustering-p2p metrics:
    • type: v_measure value: 38.680981669842135 task: type: Clustering
  • dataset: config: default name: MTEB BlurbsClusteringS2S revision: 22793b6a6465bf00120ad525e38c51210858132c split: test type: slvnwhrl/blurbs-clustering-s2s metrics:
    • type: v_measure value: 17.624489937027504 task: type: Clustering
  • dataset: config: default name: MTEB GermanDPR revision: 5129d02422a66be600ac89cd3e8531b4f97d347d split: test type: deepset/germandpr metrics:
    • type: ndcg_at_10 value: 72.921 task: type: Retrieval
  • dataset: config: default name: MTEB GermanQuAD-Retrieval revision: f5c87ae5a2e7a5106606314eef45255f03151bb3 split: test type: mteb/germanquad-retrieval metrics:
    • type: mrr_at_5 value: 85.316 task: type: Retrieval
  • dataset: config: default name: MTEB GermanSTSBenchmark revision: e36907544d44c3a247898ed81540310442329e20 split: test type: jinaai/german-STSbenchmark metrics:
    • type: cos_sim_spearman value: 84.67696933608695 task: type: STS
  • dataset: config: default name: MTEB GermanSTSBenchmark revision: e36907544d44c3a247898ed81540310442329e20 split: validation type: jinaai/german-STSbenchmark metrics:
    • type: cos_sim_spearman value: 88.048957974805 task: type: STS
  • dataset: config: de name: MTEB MassiveIntentClassification revision: 4672e20407010da34463acc759c162ca9734bca6 split: test type: mteb/amazon_massive_intent metrics:
    • type: accuracy value: 66.25084061869536 task: type: Classification
  • dataset: config: de name: MTEB MassiveIntentClassification revision: 4672e20407010da34463acc759c162ca9734bca6 split: validation type: mteb/amazon_massive_intent metrics:
    • type: accuracy value: 66.44859813084113 task: type: Classification
  • dataset: config: de name: MTEB MassiveScenarioClassification revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8 split: test type: mteb/amazon_massive_scenario metrics:
    • type: accuracy value: 72.51176866173503 task: type: Classification
  • dataset: config: de name: MTEB MassiveScenarioClassification revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8 split: validation type: mteb/amazon_massive_scenario metrics:
    • type: accuracy value: 72.02164289227742 task: type: Classification
  • dataset: config: de name: MTEB MTOPDomainClassification revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf split: test type: mteb/mtop_domain metrics:
    • type: accuracy value: 89.00253592561285 task: type: Classification
  • dataset: config: de name: MTEB MTOPDomainClassification revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf split: validation type: mteb/mtop_domain metrics:
    • type: accuracy value: 87.70798898071626 task: type: Classification
  • dataset: config: de name: MTEB MTOPIntentClassification revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba split: test type: mteb/mtop_intent metrics:
    • type: accuracy value: 70.06198929275853 task: type: Classification
  • dataset: config: de name: MTEB MTOPIntentClassification revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba split: validation type: mteb/mtop_intent metrics:
    • type: accuracy value: 68.6060606060606 task: type: Classification
  • dataset: config: de name: MTEB PawsX revision: 8a04d940a42cd40658986fdd8e3da561533a3646 split: test type: google-research-datasets/paws-x metrics:
    • type: ap value: 57.47670853851811 task: type: PairClassification
  • dataset: config: de name: MTEB PawsX revision: 8a04d940a42cd40658986fdd8e3da561533a3646 split: validation type: google-research-datasets/paws-x metrics:
    • type: ap value: 52.85587710877178 task: type: PairClassification
  • dataset: config: de name: MTEB STS22 revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 split: test type: mteb/sts22-crosslingual-sts metrics:
    • type: cos_sim_spearman value: 50.63839763951755 task: type: STS
  • dataset: config: default name: MTEB TenKGnadClusteringP2P revision: 5c59e41555244b7e45c9a6be2d720ab4bafae558 split: test type: slvnwhrl/tenkgnad-clustering-p2p metrics:
    • type: v_measure value: 37.996685796529817 task: type: Clustering
  • dataset: config: default name: MTEB TenKGnadClusteringS2S revision: 6cddbe003f12b9b140aec477b583ac4191f01786 split: test type: slvnwhrl/tenkgnad-clustering-s2s metrics:
    • type: v_measure value: 23.71145428041516 task: type: Clustering
  • dataset: config: default name: MTEB FalseFriendsGermanEnglish revision: 15d6c030d3336cbb09de97b2cefc46db93262d40 split: test type: aari1995/false_friends_de_en_mteb metrics:
    • type: ap value: 71.22096746794873 task: type: PairClassification
  • dataset: config: default name: MTEB GermanSTSBenchmark revision: e36907544d44c3a247898ed81540310442329e20 split: test type: jinaai/german-STSbenchmark metrics:
    • type: cos_sim_spearman value: 84.67698604065061 task: type: STS
  • dataset: config: default name: MTEB GermanSTSBenchmark revision: e36907544d44c3a247898ed81540310442329e20 split: validation type: jinaai/german-STSbenchmark metrics:
    • type: cos_sim_spearman value: 88.048957974805 task: type: STS

German_Semantic_STS_V2

Note: Check out my new, updated models: German_Semantic_V3 and V3b!

This model creates german embeddings for semantic use cases.

This is a sentence-transformers 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 for providing the model gBERT-large and also to Philip May for the Translation of the dataset and chats about the topic.

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

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

Then you can use the model like this:

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, 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.

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

For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net

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

The base model is trained by deepset. The dataset was published / translated by Philip May. The model was fine-tuned by Aaron Chibb.