Create README.md

README.md

@@ -0,0 +1,76 @@
+---
+language:
+- multilingual
+- ar
+- bg
+- de
+- el
+- en
+- es
+- fr
+- hi
+- it
+- ja
+- nl
+- pl
+- pt
+- ru
+- sw
+- th
+- tr
+- ur
+- vi
+- zh
+license: mit
+inference: false
+tags:
+- language
+- language-detection
+metrics:
+- accuracy
+- f1
+base_model: xlm-roberta-base
+model-index:
+- name: xlm-roberta-base-language-detection
+  results: []
+pipeline_tag: text-classification
+---
+
+# ONNX version of papluca/xlm-roberta-base-language-detection
+
+**This model is a conversion of [papluca/xlm-roberta-base-language-detection](https://huggingface.co/papluca/xlm-roberta-base-language-detection) to ONNX** format using the [🤗 Optimum](https://huggingface.co/docs/optimum/index) library.
+
+## Model description
+
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [Language Identification](https://huggingface.co/datasets/papluca/language-identification#additional-information) 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](https://huggingface.co/xlm-roberta-base) model card or to the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Conneau et al.
+
+## Intended uses & limitations
+
+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)`
+
+## Usage
+
+Loading the model requires the [🤗 Optimum](https://huggingface.co/docs/optimum/index) library installed.
+
+```python
+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)
+```