--- language: de license: mit tags: - sentence_embedding - search - pytorch - xlm-roberta - roberta - xlm-r-distilroberta-base-paraphrase-v1 - paraphrase datasets: - STSbenchmark metrics: - Spearman’s rank correlation - cosine similarity --- # German RoBERTa for Sentence Embeddings V2 **The new [T-Systems-onsite/cross-en-de-roberta-sentence-transformer](https://huggingface.co/T-Systems-onsite/cross-en-de-roberta-sentence-transformer) model is slightly better for German language. It is also the current best model for English language and works cross-lingually. Please consider using that model.** This model is intended to [compute sentence (text embeddings)](https://www.sbert.net/docs/usage/computing_sentence_embeddings.html) for German text. These embeddings can then be compared with [cosine-similarity](https://en.wikipedia.org/wiki/Cosine_similarity) to find sentences with a similar semantic meaning. For example this can be useful for [semantic textual similarity](https://www.sbert.net/docs/usage/semantic_textual_similarity.html), [semantic search](https://www.sbert.net/docs/usage/semantic_search.html), or [paraphrase mining](https://www.sbert.net/docs/usage/paraphrase_mining.html). To do this you have to use the [Sentence Transformers Python framework](https://github.com/UKPLab/sentence-transformers). > Sentence-BERT (SBERT) is a modification of the pretrained BERT network that use siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be compared using cosine-similarity. This reduces the effort for finding the most similar pair from 65hours with BERT / RoBERTa to about 5 seconds with SBERT, while maintaining the accuracy from BERT. Source: [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084) This model is fine-tuned from [Philip May](https://eniak.de/) and open-sourced by [T-Systems-onsite](https://www.t-systems-onsite.de/). Special thanks to [Nils Reimers](https://www.nils-reimers.de/) for your awesome open-source work, the Sentence Transformers, the models and your help on GitHub. ## How to use **The usage description above - provided by Hugging Face - is wrong for sentence embeddings! Please use this:** To use this model install the `sentence-transformers` package (see here: ). ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('T-Systems-onsite/german-roberta-sentence-transformer-v2') ``` For details of usage and examples see here: - [Computing Sentence Embeddings](https://www.sbert.net/docs/usage/computing_sentence_embeddings.html) - [Semantic Textual Similarity](https://www.sbert.net/docs/usage/semantic_textual_similarity.html) - [Paraphrase Mining](https://www.sbert.net/docs/usage/paraphrase_mining.html) - [Semantic Search](https://www.sbert.net/docs/usage/semantic_search.html) - [Cross-Encoders](https://www.sbert.net/docs/usage/cross-encoder.html) - [Examples on GitHub](https://github.com/UKPLab/sentence-transformers/tree/master/examples) ## Training The base model is [xlm-roberta-base](https://huggingface.co/xlm-roberta-base). This model has been further trained by [Nils Reimers](https://www.nils-reimers.de/) on a large scale paraphrase dataset for 50+ languages. [Nils Reimers](https://www.nils-reimers.de/) about this [on GitHub](https://github.com/UKPLab/sentence-transformers/issues/509#issuecomment-712243280): >A paper is upcoming for the paraphrase models. > >These models were trained on various datasets with Millions of examples for paraphrases, mainly derived from Wikipedia edit logs, paraphrases mined from Wikipedia and SimpleWiki, paraphrases from news reports, AllNLI-entailment pairs with in-batch-negative loss etc. > >In internal tests, they perform much better than the NLI+STSb models as they have see more and broader type of training data. NLI+STSb has the issue that they are rather narrow in their domain and do not contain any domain specific words / sentences (like from chemistry, computer science, math etc.). The paraphrase models has seen plenty of sentences from various domains. > >More details with the setup, all the datasets, and a wider evaluation will follow soon. The resulting model called `xlm-r-distilroberta-base-paraphrase-v1` has been released here: Building on this cross language model we fine-tuned it for German language on the [deepl.com](https://www.deepl.com/translator) dataset of our [German STSbenchmark dataset](https://github.com/t-systems-on-site-services-gmbh/german-STSbenchmark). We did an automatic hyperparameter search for 102 trials with [Optuna](https://github.com/optuna/optuna). Using 10-fold crossvalidation on the deepl.com test and dev dataset we found the following best hyperparameters: - batch_size = 15 - num_epochs = 4 - lr = 2.2995320905210864e-05 - eps = 1.8979875906303792e-06 - weight_decay = 0.003314045812507563 - warmup_steps_proportion = 0.46141685205829014 The final model was trained with these hyperparameters on the combination of `sts_de_train.csv` and `sts_de_dev.csv`. The `sts_de_test.csv` was left for testing. # Evaluation The evaluation has been done on the test set of our [German STSbenchmark dataset](https://github.com/t-systems-on-site-services-gmbh/german-STSbenchmark). The code is available on [Colab](https://colab.research.google.com/drive/1aCWOqDQx953kEnQ5k4Qn7uiixokocOHv?usp=sharing). As the metric for evaluation we use the Spearman’s rank correlation between the cosine-similarity of the sentence embeddings and STSbenchmark labels. | Model Name | Spearman rank correlation
(German) | |--------------------------------------|-------------------------------------| | xlm-r-distilroberta-base-paraphrase-v1 | 0.8079 | | xlm-r-100langs-bert-base-nli-stsb-mean-tokens | 0.8194 | | xlm-r-bert-base-nli-stsb-mean-tokens | 0.8194 | | **T-Systems-onsite/
german-roberta-sentence-transformer-v2** | **0.8529** | | **[T-Systems-onsite/
cross-en-de-roberta-sentence-transformer](https://huggingface.co/T-Systems-onsite/cross-en-de-roberta-sentence-transformer)** | **0.8550** |