Create README.md

README.md

@@ -0,0 +1,41 @@
+---
+pipeline_tag: text-classification
+language: en
+datasets:
+- valurank/wikirev-bias
+inference: false
+tags:
+- bias
+- distilroberta
+---
+
+# ONNX version of valurank/distilroberta-bias
+
+**This model is a conversion of [valurank/distilroberta-bias](https://huggingface.co/valurank/distilroberta-bias) to ONNX** format. It is designed to detect biases in text using the distilled version of the RoBERTa model. The model was converted to ONNX using the [🤗 Optimum](https://huggingface.co/docs/optimum/index) library.
+
+## Model Architecture
+
+**Base Model**: DistilRoBERTa, a distilled version of the RoBERTa model that is optimized for faster performance while maintaining similar accuracy.
+
+**Modifications**: The model is converted to ONNX format with no additional changes.
+
+## 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/distilroberta-bias-onnx")
+model = ORTModelForSequenceClassification.from_pretrained("laiyer/distilroberta-bias-onnx")
+classifier = pipeline(
+    task="text-classification",
+    model=model,
+    tokenizer=tokenizer,
+)
+
+classifier_output = classifier("Your text to analyze for bias.")
+score = (classifier_output[0]["score"] if classifier_output[0]["label"] == "BIASED" else 1 - classifier_output[0]["score"])
+```