readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - tweet_eval
  model-index:
  - name: selims
    results: []
  widget:
  - text: "I love conducting research on twins!"
    example_title: "Sentiment analysis - English"
  - text: "Ja, ik vind het tweelingen onderzoek leuk maar complex, weet je."
    example_title: "Sentiment analysis - Dutch"
  ---
  
  # selims
  
  This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) on the tweet_eval dataset.
  
  ## Model description
  
  This is a multilingual model for sentiment analysis that provides outputs ranging from 1 to 5, following the same logic as the 1 to 5-star reviews.
  
  ## Intended uses & limitations
  
  This sentiment model can be applied to datasets in the following languages: English, Dutch, German, French, Spanish, and Italian. 
  ## Training and evaluation data
  For fine-tunning of this model, the Tweet_eval dataset was used.
  
  ## Training procedure
  Please refer to the information below:
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3.0
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.1+cpu
  - Datasets 2.0.0
  - Tokenizers 0.10.3
  
  