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Model: mrm8488/distilbert-base-multi-cased-finetuned-typo-detection

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mrm8488/distilbert-base-multi-cased-finetuned-typo-detection mrm8488/distilbert-base-multi-cased-finetuned-typo-detection
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pytorch

tf

Contributed by

mrm8488 Manuel Romero
60 models

How to use this model directly from the πŸ€—/transformers library:

			
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tokenizer = AutoTokenizer.from_pretrained("mrm8488/distilbert-base-multi-cased-finetuned-typo-detection") model = AutoModelForTokenClassification.from_pretrained("mrm8488/distilbert-base-multi-cased-finetuned-typo-detection")

DISTILBERT 🌎 + Typo Detection βœβŒβœβœ”

distilbert-base-multilingual-cased fine-tuned on GitHub Typo Corpus for typo detection (using NER style)

Details of the downstream task (Typo detection as NER)

Metrics on test set πŸ“‹

Metric # score
F1 93.51
Precision 96.08
Recall 91.06

Model in action πŸ”¨

Fast usage with pipelines πŸ§ͺ

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 worksπŸŽ‰! We typed wrong Add and middleware

Created by Manuel Romero/@mrm8488

Made with in Spain