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

.gitattributes

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1_Pooling/config.json

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+{
+  "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
+}

LICENSE

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+MIT License
+
+Copyright (c) 2023 Philip May, Deutsche Telekom AG
+Copyright (c) 2022 deepset GmbH
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

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.

config.json

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+{
+  "_name_or_path": "deepset/gbert-large",
+  "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": "4.22.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 31102
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.2.2",
+    "transformers": "4.22.2",
+    "pytorch": "1.12.1+cu102"
+  }
+}

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

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sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
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+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "max_len": 512,
+  "name_or_path": "deepset/gbert-large",
+  "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]"
+}