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Model: aari1995/German_Semantic_STS_V2 Source: Original Platform
2026-08-12 12:11:17 +08:00
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+}
--- a/README.md
+++ b/README.md
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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
+- 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](https://huggingface.co/aari1995/German_Semantic_V3) and [V3b](https://huggingface.co/aari1995/German_Semantic_V3b)!
+
+This model creates german embeddings for semantic use cases.
+
+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.
--- a/config.json
+++ b/config.json
@@ -0,0 +1,25 @@
+{
+  "_name_or_path": "/content/drive/MyDrive/Stanford_NLU/Project/false_friends/gbert_large_sts_only",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.24.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 31102
+}
--- a/model.safetensors
+++ b/model.safetensors
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+size 1342992294
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--- a/special_tokens_map.json
+++ b/special_tokens_map.json
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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}
--- a/tokenizer.json
+++ b/tokenizer.json
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
@@ -0,0 +1,16 @@
+{
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "max_len": 512,
+  "name_or_path": "/content/drive/MyDrive/Stanford_NLU/Project/false_friends/gbert_large_sts_only",
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "special_tokens_map_file": null,
+  "strip_accents": false,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}
--- a/vocab.txt
+++ b/vocab.txt