readme: ---
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  # DISTILBERT 🌎 + Typo Detection ✍❌✍✔
  
  [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) fine-tuned on [GitHub Typo Corpus](https://github.com/mhagiwara/github-typo-corpus) for **typo detection** (using *NER* style)
  
  ## Details of the downstream task (Typo detection as NER)
  
  - Dataset: [GitHub Typo Corpus](https://github.com/mhagiwara/github-typo-corpus) 📚 for 15 languages
  
  - [Fine-tune script on NER dataset provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_ner_old.py) 🏋️‍♂️
  
  ## Metrics on test set 📋
  
  |  Metric   |  # score  |
  | :-------: | :-------: |
  |    F1     | **93.51** |
  | Precision | **96.08** |
  |  Recall   | **91.06** |
  
  ## Model in action 🔨
  
  Fast usage with **pipelines** 🧪
  
  ```python
  from transformers import pipeline
  
  typo_checker = pipeline(
      "ner",
      model="mrm8488/distilbert-base-multi-cased-finetuned-typo-detection",
      tokenizer="mrm8488/distilbert-base-multi-cased-finetuned-typo-detection"
  )
  
  result = typo_checker("Adddd validation midelware")
  result[1:-1]
  
  # Output:
  [{'entity': 'ok', 'score': 0.7128152847290039, 'word': 'add'},
   {'entity': 'typo', 'score': 0.5388424396514893, 'word': '##dd'},
   {'entity': 'ok', 'score': 0.94792640209198, 'word': 'validation'},
   {'entity': 'typo', 'score': 0.5839331746101379, 'word': 'mid'},
   {'entity': 'ok', 'score': 0.5195121765136719, 'word': '##el'},
   {'entity': 'ok', 'score': 0.7222476601600647, 'word': '##ware'}]
  ```
  It works🎉! We typed wrong ```Add and middleware```
  
  
  > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
  
  > Made with <span style="color: #e25555;">&hearts;</span> in Spain
  