Commit model with tokenizer

README.md

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+---
+license: apache-2.0
+---
+
+      
+<div align="center">    
+
+**⚠️ 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}
+}
+```

config.json

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+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "toxic",
+    "1": "severe_toxic",
+    "2": "obscene",
+    "3": "threat",
+    "4": "insult",
+    "5": "identity_hate"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "toxic": 0,
+    "severe_toxic": 1,
+    "obscene": 2,
+    "threat": 3,
+    "insult": 4,
+    "identity_hate": 5
+  },
+  "problem_type": "multi_label_classification",
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "type_vocab_size": 2,
+  "vocab_size": 30522
+}

model.safetensors

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+oid sha256:2c272885d24138df70bff1b3cd944a999bd6b41dad33209730aa8ba074f6ad09
+size 437975136

special_tokens_map.json

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+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

tokenizer_config.json

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+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "model_max_length": 512, "name_or_path": "bert-base-uncased"}