Create README.md

README.md

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+---
+language:
+- en
+inference: false
+pipeline_tag: token-classification
+tags:
+- toxicity
+- bias
+- roberta
+license: apache-2.0
+---
+
+# ONNX version of unitary/unbiased-toxic-roberta
+
+**This model is a conversion of [unitary/unbiased-toxic-roberta](https://huggingface.co/unitary/unbiased-toxic-roberta) to ONNX** format using the [🤗 Optimum](https://huggingface.co/docs/optimum/index) library.
+
+Trained models & code to predict toxic comments on 3 Jigsaw challenges: Toxic comment classification, Unintended Bias in Toxic comments, Multilingual toxic comment classification.
+
+Built by [Laura Hanu](https://laurahanu.github.io/) at [Unitary](https://www.unitary.ai/). 
+
+**⚠️ Disclaimer:**
+The huggingface models currently give different results to the detoxify library (see issue [here](https://github.com/unitaryai/detoxify/issues/15)). 
+
+## Labels
+All challenges have a toxicity label. The toxicity labels represent the aggregate ratings of up to 10 annotators according the following schema:
+- **Very Toxic** (a very hateful, aggressive, or disrespectful comment that is very likely to make you leave a discussion or give up on sharing your perspective)
+- **Toxic** (a rude, disrespectful, or unreasonable comment that is somewhat likely to make you leave a discussion or give up on sharing your perspective)
+- **Hard to Say**
+- **Not Toxic**
+
+More information about the labelling schema can be found [here](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data).
+
+### Toxic Comment Classification Challenge
+This challenge includes the following labels:
+
+- `toxic`
+- `severe_toxic`
+- `obscene`
+- `threat`
+- `insult`
+- `identity_hate`
+
+### Jigsaw Unintended Bias in Toxicity Classification
+This challenge has 2 types of labels: the main toxicity labels and some additional identity labels that represent the identities mentioned in the comments. 
+
+Only identities with more than 500 examples in the test set (combined public and private) are included during training as additional labels and in the evaluation calculation.
+
+- `toxicity`
+- `severe_toxicity`
+- `obscene`
+- `threat`
+- `insult`
+- `identity_attack`
+- `sexual_explicit`
+
+Identity labels used:
+- `male`
+- `female`
+- `homosexual_gay_or_lesbian`
+- `christian`
+- `jewish`
+- `muslim`
+- `black`
+- `white`
+- `psychiatric_or_mental_illness`
+
+A complete list of all the identity labels available can be found [here](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data).
+
+## 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/unbiased-toxic-roberta-onnx")
+model = ORTModelForSequenceClassification.from_pretrained("laiyer/unbiased-toxic-roberta-onnx")
+classifier = pipeline(
+    task="text-classification",
+    model=model,
+    tokenizer=tokenizer,
+)
+
+classifier_output = ner("It's not toxic comment")
+print(classifier_output)
+```