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Model: deutsche-telekom/gbert-large-paraphrase-cosine
Source: Original Platform

README.md

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+---
+pipeline_tag: sentence-similarity
+language:
+  - de
+tags:
+- sentence-transformers
+- sentence-similarity
+- transformers
+- setfit
+license: mit
+base_model: deepset/gbert-large
+datasets:
+  - deutsche-telekom/ger-backtrans-paraphrase
+
+---
+
+# German BERT large paraphrase cosine
+This is a [sentence-transformers](https://www.SBERT.net) model.
+It maps sentences & paragraphs (text) into a 1024 dimensional dense vector space.
+The model is intended to be used together with [SetFit](https://github.com/huggingface/setfit)
+to improve German few-shot text classification.
+It has a sibling model called
+[deutsche-telekom/gbert-large-paraphrase-euclidean](https://huggingface.co/deutsche-telekom/gbert-large-paraphrase-euclidean).
+
+
+This model is based on [deepset/gbert-large](https://huggingface.co/deepset/gbert-large).
+Many thanks to [deepset](https://www.deepset.ai/)!
+
+**Loss Function**\
+We have used [MultipleNegativesRankingLoss](https://www.sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss)
+with cosine similarity as the loss function.
+
+**Training Data**\
+The model is trained on a carefully filtered dataset of
+[deutsche-telekom/ger-backtrans-paraphrase](https://huggingface.co/datasets/deutsche-telekom/ger-backtrans-paraphrase).
+We deleted the following pairs of sentences:
+- `min_char_len` less than 15
+- `jaccard_similarity` greater than 0.3
+- `de_token_count` greater than 30
+- `en_de_token_count` greater than 30
+- `cos_sim` less than 0.85
+
+**Hyperparameters**
+- learning_rate: 8.345726930229726e-06
+- num_epochs: 7
+- train_batch_size: 57
+- num_gpu: 1
+
+## Evaluation Results
+We use the [NLU Few-shot Benchmark - English and German](https://huggingface.co/datasets/deutsche-telekom/NLU-few-shot-benchmark-en-de)
+dataset to evaluate this model in a German few-shot scenario.
+
+**Qualitative results**
+- multilingual sentence embeddings provide the worst results
+- Electra models also deliver poor results
+- German BERT base size model ([deepset/gbert-base](https://huggingface.co/deepset/gbert-base)) provides good results
+- German BERT large size model ([deepset/gbert-large](https://huggingface.co/deepset/gbert-large)) provides very good results
+- our fine-tuned models (this model and [deutsche-telekom/gbert-large-paraphrase-euclidean](https://huggingface.co/deutsche-telekom/gbert-large-paraphrase-euclidean)) provide best results
+
+## Licensing
+Copyright (c) 2023 [Philip May](https://may.la/), [Deutsche Telekom AG](https://www.telekom.com/)\
+Copyright (c) 2022 [deepset GmbH](https://www.deepset.ai/)
+
+Licensed under the **MIT License** (the "License"); you may not use this file except in compliance with the License.
+You may obtain a copy of the License by reviewing the file
+[LICENSE](https://huggingface.co/deutsche-telekom/gbert-large-paraphrase-cosine/blob/main/LICENSE) in the repository.