Create README.md

README.md

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+---
+license: mit
+language:
+- ru
+metrics:
+- f1
+- roc_auc
+- precision
+- recall
+pipeline_tag: text-classification
+tags:
+- emotion-classification
+- emotion-recognition
+- multi-label-classification
+- emotion
+- russian
+- rubert
+- tiny
+- sentiment
+- sentiment-analysis
+- classification
+- multilabel
+- bert
+datasets:
+- cedr
+---
+
+This is [RuBERT-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) model fine-tuned for __emotion classification__ of short __Russian__ texts.
+The task is a __multi-label classification__ with the following labels:
+
+```yaml
+0: no_emotion
+1: joy
+2: sadness
+3: surprise
+4: fear
+5: anger
+```
+
+Label to Russian label:
+
+```yaml
+no_emotion: нет эмоции
+joy: радость
+sadness: грусть
+surprise: удивление
+fear: страх
+anger: злость
+```
+
+## Usage
+
+```python
+from transformers import pipeline
+model = pipeline(model="seara/rubert-tiny2-cedr")
+model("Привет, ты мне нравишься!")
+# [{'label': 'joy', 'score': 0.9605025053024292}]
+```
+
+## Dataset
+
+This model was trained on the [CEDR dataset](https://huggingface.co/datasets/cedr).
+
+An overview of the training data can be found in the source [article](https://www.sciencedirect.com/science/article/pii/S1877050921013247).
+
+## Training
+
+Training were done in this [project](https://github.com/searayeah/vkr-bert) with this parameters:
+
+```yaml
+tokenizer.max_length: null
+batch_size: 64
+optimizer: adam
+lr: 0.00001
+weight_decay: 0
+num_epochs: 30
+```
+
+## Eval results (on test split)
+
+|         |no_emotion|joy   |sadness|surprise|fear   |anger|micro avg|macro avg|weighted avg|
+|---------|----------|------|-------|--------|-------|-----|---------|---------|------------|
+|precision|0.82      |0.84  |0.84   |0.79    |0.78   |0.55 |0.81     |0.77     |0.8         |
+|recall   |0.84      |0.83  |0.85   |0.66    |0.67   |0.33 |0.78     |0.7      |0.78        |
+|f1-score |0.83      |0.83  |0.84   |0.72    |0.72   |0.41 |0.79     |0.73     |0.79        |
+|auc-roc  |0.92      |0.96  |0.96   |0.91    |0.91   |0.77 |0.94     |0.91     |0.93        |
+|support  |734       |353   |379    |170     |141    |125  |1902     |1902     |1902        |