Create README.md

README.md

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+---
+language: ["ru"]
+tags:
+- russian
+- classification
+- sentiment
+- multiclass
+widget:
+- text: "Какая гадость эта ваша заливная рыба!"
+---
+This is the [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny) model fine-tuned for classification of sentiment for short Russian texts. 
+
+The problem is formulated as multiclass classification: `negative` vs `neutral` vs `positive`. 
+## Usage
+
+The function below estimates the sentiment of the given text:
+```python
+# !pip install transformers sentencepiece --quiet
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+model_checkpoint = 'cointegrated/rubert-tiny-sentiment-balanced'
+tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)
+if torch.cuda.is_available():
+    model.cuda()
+    
+def get_sentiment(text, return_type='label'):
+    """ Calculate sentiment of a text. `return_type` can be 'label', 'score' or 'proba' """
+    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()[0]
+    if return_type == 'label':
+        return model.config.id2label[proba.argmax()]
+    elif return_type == 'score':
+        return proba.dot([-1, 0, 1])
+    return proba
+
+text = 'Какая гадость эта ваша заливная рыба!'
+# classify the text
+print(get_sentiment(text, 'label'))  # negative
+# score the text on the scale from -1 (very negative) to +1 (very positive)
+print(get_sentiment(text, 'score'))  # -0.5894946306943893
+# calculate probabilities of all labels
+print(get_sentiment(text, 'proba'))  # [0.7870447  0.4947824  0.19755007]
+```
+
+## Training
+
+We trained the model on [the datasets collected by Smetanin](https://github.com/sismetanin/sentiment-analysis-in-russian). We have converted all training data into a 3-class format and have up- and downsampled the training data to balance both the sources and the classes. The training code is available as [a Colab notebook](https://gist.github.com/avidale/e678c5478086c1d1adc52a85cb2b93e6). The metrics on the balanced test set are the following: 
+
+
+| Source | Macro F1 |
+| ----------- | ----------- |
+| SentiRuEval2016_banks | 0.83 | 
+| SentiRuEval2016_tele  | 0.74 |
+| kaggle_news | 0.66 |
+| linis | 0.50 |
+| mokoron | 0.98 |
+| rureviews | 0.72 |
+| rusentiment | 0.67 | 
+