[!WARNING] THIS PROJECT HAS BEEN ARCHIVED.
This project and its associated code on GitHub are no longer under active development or maintained.
This model is a conversion of papluca/xlm-roberta-base-language-detection to ONNX format using the 🤗 Optimum library.
This model is a fine-tuned version of xlm-roberta-base on the Language Identification dataset.
This model is an XLM-RoBERTa transformer model with a classification head on top (i.e. a linear layer on top of the pooled output). For additional information please refer to the xlm-roberta-base model card or to the paper Unsupervised Cross-lingual Representation Learning at Scale by Conneau et al.
You can directly use this model as a language detector, i.e. for sequence classification tasks. Currently, it supports the following 20 languages:
arabic (ar), bulgarian (bg), german (de), modern greek (el), english (en), spanish (es), french (fr), hindi (hi), italian (it), japanese (ja), dutch (nl), polish (pl), portuguese (pt), russian (ru), swahili (sw), thai (th), turkish (tr), urdu (ur), vietnamese (vi), and chinese (zh)
Loading the model requires the 🤗 Optimum library installed.
from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import AutoTokenizer, pipeline
tokenizer = AutoTokenizer.from_pretrained("laiyer/xlm-roberta-base-language-detection-onnx")
model = ORTModelForSequenceClassification.from_pretrained("laiyer/xlm-roberta-base-language-detection-onnx")
classifier = pipeline(
task="text-classification",
model=model,
tokenizer=tokenizer,
top_k=None,
)
classifier_output = ner("It's not toxic comment")
print(classifier_output)
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