Create README.md

README.md

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+---
+language: ["ru", "en"]
+tags:
+- russian
+- classification
+- toxicity
+- multilabel
+widget:
+- text: "Иди ты нафиг!"
+---
+This is the [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny) model fine-tuned for classification of toxicity and inappropriateness. 
+
+The problem is formulated as multilabel classification with the following classes:
+- `non-toxic`: the text does NOT contain insults, obscenities, and threats, in the sense of the [OK ML Cup](https://cups.mail.ru/ru/tasks/1048) competition.
+- `insult`
+- `obscenity`
+- `threat`
+- `dangerous`: the text is inappropriate, in the sense of [Babakov et.al.](https://arxiv.org/abs/2103.05345), i.e. it can harm the reputation of the speaker.
+
+A text can be considered safe if it is BOTH `non-toxic` and NOT `dangerous`. 
+
+## Usage
+
+The function below estimates the probability that the text is either toxic OR dangerous:
+```python
+# !pip install transformers sentencepiece --quiet
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+model_checkpoint = 'cointegrated/rubert-tiny-toxicity'
+tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)
+if torch.cuda.is_available():
+    model.cuda()
+    
+def text2toxicity(text, aggregate=True):
+    """ Calculate toxicity of a text (if aggregate=True) or a vector of toxicity aspects (if aggregate=False)"""
+    with torch.no_grad():
+        inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True).to(model.device)
+        proba = torch.sigmoid(model(**inputs).logits).cpu().numpy()
+    if isinstance(text, str):
+        proba = proba[0]
+    if aggregate:
+        return 1 - proba.T[0] * (1 - proba.T[-1])
+    return proba
+
+print(text2toxicity('я люблю нигеров', True))
+# 0.57240640889815
+
+print(text2toxicity('я люблю нигеров', False))
+# [9.9336821e-01 6.1555761e-03 1.2781911e-03 9.2758919e-04 5.6955177e-01]
+
+print(text2toxicity(['я люблю нигеров', 'я люблю африканцев'], True))
+# [0.5724064  0.20111847]
+
+print(text2toxicity(['я люблю нигеров', 'я люблю африканцев'], False))
+# [[9.9336821e-01 6.1555761e-03 1.2781911e-03 9.2758919e-04 5.6955177e-01]
+#  [9.9828428e-01 1.1138428e-03 1.1492912e-03 4.6551935e-04 1.9974548e-01]]
+```
+
+## Training
+
+The model has been training on the joint dataset of [OK ML Cup](https://cups.mail.ru/ru/tasks/1048) and [Babakov et.al.](https://arxiv.org/abs/2103.05345) with `Adam` optimizer, learning rate of `1e-5`, and batch size of `128` for `5` epochs. The data was not filtered in any way. A text was considered inappropriate if its inappropritateness score was higher than 0.2. The per-label ROC AUC on the dev set is:
+```
+non-toxic  : 0.9909
+insult     : 0.9882
+obscenity  : 0.9824
+threat     : 0.9868
+dangerous  : 0.7758
+```