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            The ImranzamanML/GEFS-language-detector is a fined tuned model by using the dataset of papluca [Language Identification](https://huggingface.co/datasets/papluca/language-identification#additional-information) and the base model [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) .
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            ## Supported languages
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            Currently this model support 4 languages  | 
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            <iframe width="891" height="501" src="https://www.youtube.com/embed/TjQhobopnrA" title="Theum in 2 Minutes" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
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            Following languages supported by the model:
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            - german (de)
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            - spanish (es)
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            - french (fr)
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            ## Training | 
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                Epoch	  Training Loss	    Validation Loss
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                1	      0.002600	        0.000148  
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                fr	       0.9995	   0.9996	0.9996	 0.9996
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                es	       0.9994	   0.9996	0.9995	 0.9996
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            The ImranzamanML/GEFS-language-detector is a fined tuned model by using the dataset of papluca [Language Identification](https://huggingface.co/datasets/papluca/language-identification#additional-information) and the base model [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) .
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            This language detection model demonstrated exceptional performance, achieving an impressive F1 score close to 100%. This result significantly exceeds typical benchmarks and underscores the model's accuracy and reliability in identifying languages.
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            ## Supported languages
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            Currently this model support 4 languages but in future more languages will be added. 
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            Following languages supported by the model:
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            - german (de)
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            - spanish (es)
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            - french (fr)
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            ## Model Training
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                Epoch	  Training Loss	    Validation Loss
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                1	      0.002600	        0.000148  
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                fr	       0.9995	   0.9996	0.9996	 0.9996
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                es	       0.9994	   0.9996	0.9995	 0.9996
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