Create README.md

README.md

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+---
+license: mit
+language:
+- ru
+metrics:
+- f1
+- roc_auc
+- precision
+- recall
+pipeline_tag: text-classification
+tags:
+- rubert
+- emotion
+- emotion-classification
+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
+```
+
+## 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|samples avg|
+|---------|----------|------|-------|--------|-------|------|---------|---------|------------|-----------|
+|precision|0.8176    |0.8371|0.8425 |0.7902  |0.7833 |0.5467|0.811    |0.7696   |0.8034      |0.7811     |
+|recall   |0.8365    |0.83  |0.847  |0.6647  |0.6667 |0.328 |0.776    |0.6955   |0.776       |0.7792     |
+|f1-score |0.8269    |0.8336|0.8447 |0.722   |0.7203 |0.41  |0.7931   |0.7263   |0.787       |0.7788     |
+|support  |734.0     |353.0 |379.0  |170.0   |141.0  |125.0 |1902.0   |1902.0   |1902.0      |1902.0     |
+|auc-roc  |0.9241    |0.9649|0.9557 |0.913   |0.9118 |0.7732|0.9355   |0.9071   |0.9261      |           |