metadata
license: apache-2.0
datasets:
- papluca/language-identification
language:
- en
- de
- fr
- es
metrics:
- precision
- recall
- f1
- accuracy
pipeline_tag: text-classification
German, English, French and Spanish Language Detector
The GEFS-language-detector language 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. This is a fined tuned model by using the dataset of papluca Language Identification and the base model xlm-roberta-base .
Predicted output:
Model will return the language detection in the language codes like:
- de as German
- en as English
- fr as French
- es as Spanish
Supported languages
Currently this model support 4 languages but in future more languages will be added.
Following languages supported by the model:
- German (de)
- English (en)
- French (fr)
- Spanish (es)
Use a pipeline as a high-level helper
from transformers import pipeline
text=["Mir gefällt die Art und Weise, Sprachen zu erkennen",
"I like the way to detect languages",
"Me gusta la forma de detectar idiomas",
"J'aime la façon de détecter les langues"]
pipe = pipeline("text-classification", model="ImranzamanML/GEFS-language-detector")
lang_detect=pipe(text, top_k=1)
print("The detected language is", lang_detect)
Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("ImranzamanML/GEFS-language-detector")
model = AutoModelForSequenceClassification.from_pretrained("ImranzamanML/GEFS-language-detector")
Model Training
Epoch Training Loss Validation Loss
1 0.002600 0.000148
2 0.001000 0.000015
3 0.000000 0.000011
4 0.001800 0.000009
5 0.002700 0.000016
6 0.001600 0.000012
7 0.001300 0.000009
8 0.001200 0.000008
9 0.000900 0.000007
10 0.000900 0.000007
Testing Results
Language Precision Recall F1 Accuracy
de 0.9997 0.9998 0.9998 0.9999
en 1.0000 1.0000 1.0000 1.0000
fr 0.9995 0.9996 0.9996 0.9996
es 0.9994 0.9996 0.9995 0.9996
About Author
Name: Muhammad Imran Zaman Company: Theum AG Role: Machine Learning Engineer
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