Update README.md

README.md

@@ -6,7 +6,55 @@ tags:
 - russian
 - classification
 - toxicity
-- binary
 widget:
 - text: Нелепые лохи недовольны всегда и всем
----
+---
+
+Bert-based classifier (finetuned from [rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2)) 
+
+Merged datasets:
+
+- [Russian Language Toxic Comments from 2ch.hk and pikabu.ru](https://www.kaggle.com/datasets/blackmoon/russian-language-toxic-comments)
+- [Toxic Russian Comments from ok.ru](https://www.kaggle.com/datasets/alexandersemiletov/toxic-russian-comments)
+
+The datasets split into train, val, test splits in 80-10-10 proportion
+The metrics obtained from test dataset is as follows:
+|        |precision|recall|f1-score|support|
+|--------|---------|------|--------|-------|
+|0       |0.9827   |0.9827|0.9827  |21216  |
+|1       |0.9272   |0.9274|0.9273  |5054   |
+|        |         |      |        |       |
+|accuracy|         |      |0.9720  |26270  |
+|macro avg|0.9550   |0.9550|0.9550  |26270  |
+|weighted avg|0.9720   |0.9720|0.9720  |26270  |
+
+### Usage
+
+```Python
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+PATH = 'khvatov/ru_toxicity_detector'
+tokenizer = AutoTokenizer.from_pretrained(PATH)
+model = AutoModelForSequenceClassification.from_pretrained(PATH)
+
+# if torch.cuda.is_available():
+#     model.cuda()
+
+model.to(torch.device("cpu"))
+
+
+def get_toxicity_probs(text):
+    with torch.no_grad():
+        inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True).to(model.device)
+        proba = torch.nn.functional.softmax(model(**inputs).logits, dim=1).cpu().numpy()
+    return proba[0]
+
+
+TEXT = "Марк был хороший"
+print(f'text = {TEXT}, probs={get_toxicity_probs(TEXT)}')
+# text = Марк был хороший, probs=[0.9940585  0.00594147]
+```
+
+### Train
+The model has been trained with Adam optimizer, the learning rate of 2e-5, and batch size of 32 for 3 epochs