readme: ---
  pipeline_tag: text-classification
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - Setfit
  
  ---
  
  # {Setfit_youtube_comments}
  
  This is a [Setfit](https://github.com/huggingface/setfit) model: It maps sentences to a n dimensional dense vector space and
  can be used for classification of text into question or not_question class.
  
  <!--- Describe your model here -->
  
  ## Usage (Sentence-Transformers)
  
  Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) and  setfit installed:
  
  ```
  pip install -U sentence-transformers
  pip install setfit
  ```
  
  Then you can use the model like this:
  
  ```python
  from setfit import SetFitModel
  model = SetFitModel.from_pretrained("tushifire/setfit_youtube_comments_is_a_question")
  
  # Run inference
  preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
  print(preds)
  
  preds = model(["""what video do I watch that takes the html_output and insert it into the actual html page?""",
                 "Why does for loop end without a break statement"])
  print(preds)
  ```
  
  
  ## Training
  The model was trained with the parameters:
  
  **DataLoader**:
  
  `torch.utils.data.dataloader.DataLoader` of length 80 with parameters:
  ```
  {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
  ```
  
  **Loss**:
  
  `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 
  
  Parameters of the fit()-Method:
  ```
  {
      "epochs": 10,
      "evaluation_steps": 0,
      "evaluator": "NoneType",
      "max_grad_norm": 1,
      "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
      "optimizer_params": {
          "lr": 2e-05
      },
      "scheduler": "WarmupLinear",
      "steps_per_epoch": 800,
      "warmup_steps": 80,
      "weight_decay": 0.01
  }
  ```
  
  
  ## Full Model Architecture
  ```
  SentenceTransformer(
    (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
    (2): Normalize()
  )
  ```
  
  ## Citing & Authors
  
  <!--- Describe where people can find more information -->