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