update readme

README.md

@@ -1,135 +1,45 @@
 ---
 pipeline_tag: sentence-similarity
+language:
+  - de
 tags:
 - sentence-transformers
-- feature-extraction
 - sentence-similarity
 - transformers
+- setfit
+license: mit
+datasets:
+  - deutsche-telekom/ger-backtrans-paraphrase
 
 ---
 
-# {MODEL_NAME}
-
-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.
-
-<!--- 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('{MODEL_NAME}')
-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('{MODEL_NAME}')
-model = AutoModel.from_pretrained('{MODEL_NAME}')
-
-# 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})
+# 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.
 
+This model is based on [deepset/gbert-large](https://huggingface.co/deepset/gbert-large).
+Many thanks to [deepset](https://www.deepset.ai/)!
 
 ## Training
-The model was trained with the parameters:
+TODO
 
-**DataLoader**:
-
-`torch.utils.data.dataloader.DataLoader` of length 44527 with parameters:
-```
-{'batch_size': 68, 'sampler': 'torch.utils.data.dataloader._InfiniteConstantSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
-```
-
-**Loss**:
-
-`sentence_transformers.losses.BatchHardSoftMarginTripletLoss.BatchHardSoftMarginTripletLoss` 
-
-Parameters of the fit()-Method:
-```
-{
-    "epochs": 7,
-    "evaluation_steps": 8905,
-    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
-    "max_grad_norm": 1,
-    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
-    "optimizer_params": {
-        "lr": 5.5512022294147105e-06
-    },
-    "scheduler": "WarmupLinear",
-    "steps_per_epoch": null,
-    "warmup_steps": 93507,
-    "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
+## 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.
 
-<!--- Describe where people can find more information -->
+**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-cosine](https://huggingface.co/deutsche-telekom/gbert-large-paraphrase-cosine)) 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]() in the repository.
+[LICENSE](https://huggingface.co/deutsche-telekom/gbert-large-paraphrase-cosine/blob/main/LICENSE) in the repository.