readme: ---
  pipeline_tag: sentence-similarity
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  ---
  
  # SBERT-base-nli-v2-bitfit
  
  ## Usage
  
  For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt 
  
  ## Evaluation Results
  
  For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
  
  ## Training
  The model was trained with the parameters:
  
  **DataLoader**:
  
  `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8807 with parameters:
  ```
  {'batch_size': 64}
  ```
  
  **Loss**:
  
  `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
    ```
    {'scale': 20.0, 'similarity_fct': 'cos_sim'}
    ```
  
  Parameters of the fit()-Method:
  ```
  {
      "epochs": 1,
      "evaluation_steps": 880,
      "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
      "max_grad_norm": 1,
      "optimizer_class": "<class 'transformers.optimization.AdamW'>",
      "optimizer_params": {
          "lr": 0.0002
      },
      "scheduler": "WarmupLinear",
      "steps_per_epoch": null,
      "warmup_steps": 881,
      "weight_decay": 0.01
  }
  ```
  
  
  ## Full Model Architecture
  ```
  SentenceTransformer(
    (0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: BertModel 
    (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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
  )
  ```
  
  ## Citing & Authors
  
  ```bibtex
  @article{muennighoff2022sgpt,
    title={SGPT: GPT Sentence Embeddings for Semantic Search},
    author={Muennighoff, Niklas},
    journal={arXiv preprint arXiv:2202.08904},
    year={2022}
  }
  ```
  