readme: ---
  pipeline_tag: sentence-similarity
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  - german
  - nli
  - text-classification
  
  
  ---
  
  # airnicco8/xlm-roberta-de
  
  This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. It is trained on the [Ted talks transcripts](https://www.kaggle.com/datasets/rounakbanik/ted-talks) filtered only by German language, the training setting is described [here](https://towardsdatascience.com/a-complete-guide-to-transfer-learning-from-english-to-other-languages-using-sentence-embeddings-8c427f8804a9). It can be used straight-forwardly for sentence similarity, but can also be fine-tuned for NLI and Text-Classification, examples coming soon.
  
  ## 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 = ["das ist eine glückliche Frau", "das ist ein glücklicher Mann", "das ist ein glücklicher Hund"]
  
  model = SentenceTransformer('airnicco8/xlm-roberta-de')
  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 = ["das ist eine glückliche Frau", "das ist ein glücklicher Mann", "das ist ein glücklicher Hund"]
  
  # Load model from HuggingFace Hub
  tokenizer = AutoTokenizer.from_pretrained('airnicco8/xlm-roberta-de')
  model = AutoModel.from_pretrained('airnicco8/xlm-roberta-de')
  
  # 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=airnicco8/xlm-roberta-de)
  
  
  ## Training
  The model was trained with the parameters:
  
  **DataLoader**:
  
  `torch.utils.data.dataloader.DataLoader` of length 3071 with parameters:
  ```
  {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
  ```
  
  **Loss**:
  
  `sentence_transformers.losses.MSELoss.MSELoss` 
  
  Parameters of the fit()-Method:
  ```
  {
      "epochs": 10,
      "evaluation_steps": 1000,
      "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
      "max_grad_norm": 1,
      "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
      "optimizer_params": {
          "eps": 1e-06,
          "lr": 2e-05
      },
      "scheduler": "WarmupLinear",
      "steps_per_epoch": null,
      "warmup_steps": 1000,
      "weight_decay": 0.01
  }
  ```
  
  
  ## Full Model Architecture
  ```
  SentenceTransformer(
    (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
  )
  ```
  
  ## Citing & Authors
  
  <!--- Describe where people can find more information -->