hoan
/

multilingual-toxic-xlm-roberta-dynamic-quantized

@@ -5,330 +5,39 @@ tags:
 - toxicity
 - toxic detection
 ---
-<div align="center">
-This model card is extracted from the original model: [https://huggingface.co/unitary/multilingual-toxic-xlm-roberta](https://huggingface.co/unitary/multilingual-toxic-xlm-roberta)
-**⚠️ Disclaimer:**
-The huggingface models currently give different results to the detoxify library (see issue [here](https://github.com/unitaryai/detoxify/issues/15)). For the most up to date models we recommend using the models from https://github.com/unitaryai/detoxify
-# 🙊 Detoxify
-##  Toxic Comment Classification with ⚡ Pytorch Lightning and 🤗 Transformers
-![CI testing](https://github.com/unitaryai/detoxify/workflows/CI%20testing/badge.svg)
-![Lint](https://github.com/unitaryai/detoxify/workflows/Lint/badge.svg)
-</div>
-![Examples image](examples.png)
-## Description
-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/), where we are working to stop harmful content online by interpreting visual content in context.
-Dependencies:
-- For inference:
-  - 🤗 Transformers
-  - ⚡ Pytorch lightning
-- For training will also need:
-  - Kaggle API (to download data)
-| Challenge | Year | Goal | Original Data Source | Detoxify Model Name | Top Kaggle Leaderboard Score | Detoxify Score
-|-|-|-|-|-|-|-|
-| [Toxic Comment Classification Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge) | 2018 |  build a multi-headed model that’s capable of detecting different types of of toxicity like threats, obscenity, insults, and identity-based hate. | Wikipedia Comments | `original` | 0.98856 | 0.98636
-| [Jigsaw Unintended Bias in Toxicity Classification](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification) | 2019 | build a model that recognizes toxicity and minimizes this type of unintended bias with respect to mentions of identities. You'll be using a dataset labeled for identity mentions and optimizing a metric designed to measure unintended bias. | Civil Comments | `unbiased` | 0.94734 | 0.93639
-| [Jigsaw Multilingual Toxic Comment Classification](https://www.kaggle.com/c/jigsaw-multilingual-toxic-comment-classification) | 2020 | build effective multilingual models | Wikipedia Comments + Civil Comments | `multilingual` | 0.9536 | 0.91655*
-*Score not directly comparable since it is obtained on the validation set provided and not on the test set. To update when the test labels are made available.
-It is also noteworthy to mention that the top leadearboard scores have been achieved using model ensembles. The purpose of this library was to build something user-friendly and straightforward to use.
-## Limitations and ethical considerations
-If words that are associated with swearing, insults or profanity are present in a comment, it is likely that it will be classified as toxic, regardless of the tone or the intent of the author e.g. humorous/self-deprecating. This could present some biases towards already vulnerable minority groups.
-The intended use of this library is for research purposes, fine-tuning on carefully constructed datasets that reflect real world demographics  and/or to aid content moderators in flagging out harmful content quicker.
-Some useful resources about the risk of different biases in toxicity or hate speech detection are:
-- [The Risk of Racial Bias in Hate Speech Detection](https://homes.cs.washington.edu/~msap/pdfs/sap2019risk.pdf)
-- [Automated Hate Speech Detection and the Problem of Offensive Language](https://arxiv.org/pdf/1703.04009.pdf%201.pdf)
-- [Racial Bias in Hate Speech and Abusive Language Detection Datasets](https://arxiv.org/pdf/1905.12516.pdf)
-## Quick prediction
-The `multilingual` model has been trained on 7 different languages so it should only be tested on: `english`, `french`, `spanish`, `italian`, `portuguese`, `turkish` or `russian`.
-```bash
-# install detoxify
-pip install detoxify
-```
-```python
-from detoxify import Detoxify
-# each model takes in either a string or a list of strings
-results = Detoxify('original').predict('example text')
-results = Detoxify('unbiased').predict(['example text 1','example text 2'])
-results = Detoxify('multilingual').predict(['example text','exemple de texte','texto de ejemplo','testo di esempio','texto de exemplo','örnek metin','пример текста'])
-# optional to display results nicely (will need to pip install pandas)
-import pandas as pd
-print(pd.DataFrame(results, index=input_text).round(5))
-```
-For more details check the Prediction section.
-## 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).
-### Jigsaw Multilingual Toxic Comment Classification
-Since this challenge combines the data from the previous 2 challenges, it includes all labels from above, however the final evaluation is only on:
-- `toxicity`
-## How to run
-First, install dependencies
-```bash
-# clone project
-git clone https://github.com/unitaryai/detoxify
-# create virtual env
-python3 -m venv toxic-env
-source toxic-env/bin/activate
-# install project
-pip install -e detoxify
-cd detoxify
-# for training
-pip install -r requirements.txt
- ```
-## Prediction
-Trained models summary:
-|Model name| Transformer type| Data from
-|:--:|:--:|:--:|
-|`original`| `bert-base-uncased` | Toxic Comment Classification Challenge
-|`unbiased`| `roberta-base`| Unintended Bias in Toxicity Classification
-|`multilingual`| `xlm-roberta-base`| Multilingual Toxic Comment Classification
-For a quick prediction can run the example script on a comment directly or from a txt containing a list of comments.
-```bash
-# load model via torch.hub
-python run_prediction.py --input 'example' --model_name original
-# load model from from checkpoint path
-python run_prediction.py --input 'example' --from_ckpt_path model_path
-# save results to a .csv file
-python run_prediction.py --input test_set.txt --model_name original --save_to results.csv
-# to see usage
-python run_prediction.py --help
-```
-Checkpoints can be downloaded from the latest release or via the Pytorch hub API with the following names:
-- `toxic_bert`
-- `unbiased_toxic_roberta`
-- `multilingual_toxic_xlm_r`
-```bash
-model = torch.hub.load('unitaryai/detoxify','toxic_bert')
-```
-Importing detoxify in python:
-```python
-from detoxify import Detoxify
-results = Detoxify('original').predict('some text')
-results = Detoxify('unbiased').predict(['example text 1','example text 2'])
-results = Detoxify('multilingual').predict(['example text','exemple de texte','texto de ejemplo','testo di esempio','texto de exemplo','örnek metin','пример текста'])
-# to display results nicely
-import pandas as pd
-print(pd.DataFrame(results,index=input_text).round(5))
-```
-## Training
- If you do not already have a Kaggle account:
- - you need to create one to be able to download the data
- - go to My Account and click on Create New API Token - this will download a kaggle.json file
- - make sure this file is located in ~/.kaggle
- ```bash
-# create data directory
-mkdir jigsaw_data
-cd jigsaw_data
-# download data
-kaggle competitions download -c jigsaw-toxic-comment-classification-challenge
-kaggle competitions download -c jigsaw-unintended-bias-in-toxicity-classification
-kaggle competitions download -c jigsaw-multilingual-toxic-comment-classification
-```
-## Start Training
- ### Toxic Comment Classification Challenge
- ```bash
-python create_val_set.py
-python train.py --config configs/Toxic_comment_classification_BERT.json
-```
- ### Unintended Bias in Toxicicity Challenge
-```bash
-python train.py --config configs/Unintended_bias_toxic_comment_classification_RoBERTa.json
-```
- ### Multilingual Toxic Comment Classification
- This is trained in 2 stages. First, train on all available data, and second, train only on the translated versions of the first challenge.
- The [translated data](https://www.kaggle.com/miklgr500/jigsaw-train-multilingual-coments-google-api) can be downloaded from Kaggle in french, spanish, italian, portuguese, turkish, and russian (the languages available in the test set).
- ```bash
-# stage 1
-python train.py --config configs/Multilingual_toxic_comment_classification_XLMR.json
-# stage 2
-python train.py --config configs/Multilingual_toxic_comment_classification_XLMR_stage2.json
-```
-### Monitor progress with tensorboard
- ```bash
-tensorboard --logdir=./saved
-```
-## Model Evaluation
-### Toxic Comment Classification Challenge
-This challenge is evaluated on the mean AUC score of all the labels.
-```bash
-python evaluate.py --checkpoint saved/lightning_logs/checkpoints/example_checkpoint.pth --test_csv test.csv
-```
-### Unintended Bias in Toxicicity Challenge
-This challenge is evaluated on a novel bias metric that combines different AUC scores to balance overall performance. More information on this metric [here](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/overview/evaluation).
-```bash
-python evaluate.py --checkpoint saved/lightning_logs/checkpoints/example_checkpoint.pth --test_csv test.csv
-# to get the final bias metric
-python model_eval/compute_bias_metric.py
-```
-### Multilingual Toxic Comment Classification
-This challenge is evaluated on the AUC score of the main toxic label.
-```bash
-python evaluate.py --checkpoint saved/lightning_logs/checkpoints/example_checkpoint.pth --test_csv test.csv
-```
-### Citation
 ```
-@misc{Detoxify,
-  title={Detoxify},
-  author={Hanu, Laura and {Unitary team}},
-  howpublished={Github. https://github.com/unitaryai/detoxify},
-  year={2020}
-}
-```

 - toxicity
 - toxic detection
 ---
+This ONNX model is a dynamic quantized for the original model: [https://huggingface.co/unitary/multilingual-toxic-xlm-roberta](https://huggingface.co/unitary/multilingual-toxic-xlm-roberta)
+### Usage
+Using `pipeline` from the `optimum` library
+```
+from optimum.pipelines import pipeline as pipeline_onnx
+quantized_pipeline = pipeline_onnx(
+  "text-classification",
+  model="hoan/multilingual-toxic-xlm-roberta-dynamic-quantized",
+  accelerator="ort",
+  top_k=None)
+text = """Artificial intelligence (AI), frequently depicted in mainstream media as a harbinger of both groundbreaking innovation and understandable concern, has seamlessly permeated and embedded itself within a multitude of diverse sectors that constitute the intricate tapestry of our contemporary society. This relentless integration spans a wide spectrum, extending from the realms of healthcare, where AI is catalyzing transformative breakthroughs in disease diagnosis, treatment planning, and medical research, to the intricate domain of finance, where algorithms are reshaping the landscape of investment strategies, risk assessment, and market predictions."""
+result = quantized_pipeline(text)
+```
+The result is:
+```
+[[{'label': 'LABEL_0', 'score': 0.20129913091659546},
+  {'label': 'LABEL_7', 'score': 0.15916699171066284},
+  {'label': 'LABEL_8', 'score': 0.11170786619186401},
+  {'label': 'LABEL_4', 'score': 0.08739417046308517},
+  {'label': 'LABEL_14', 'score': 0.07075755298137665},
+  {'label': 'LABEL_10', 'score': 0.06712991744279861},
+  {'label': 'LABEL_2', 'score': 0.06049202010035515},
+  {'label': 'LABEL_13', 'score': 0.05316930636763573},
+  {'label': 'LABEL_3', 'score': 0.04292014613747597},
+  {'label': 'LABEL_12', 'score': 0.041316740214824677},
+  {'label': 'LABEL_15', 'score': 0.026307238265872},
+  {'label': 'LABEL_9', 'score': 0.024316439405083656},
+  {'label': 'LABEL_11', 'score': 0.017735131084918976},
+  {'label': 'LABEL_5', 'score': 0.015682993456721306},
+  {'label': 'LABEL_6', 'score': 0.01122154202312231},
+  {'label': 'LABEL_1', 'score': 0.009382889606058598}]]
 ```