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--- |
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license: mit |
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language: |
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- ru |
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metrics: |
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- f1 |
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- roc_auc |
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- precision |
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- recall |
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pipeline_tag: text-classification |
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tags: |
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- sentiment-analysis |
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- multi-label-classification |
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- sentiment analysis |
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- rubert |
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- sentiment |
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- bert |
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- tiny |
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- russian |
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- multilabel |
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- classification |
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- emotion-classification |
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- emotion-recognition |
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- emotion |
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- emotion-detection |
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datasets: |
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- cedr |
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--- |
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This is [RuBERT](https://huggingface.co/DeepPavlov/rubert-base-cased) model fine-tuned for __emotion classification__ of short __Russian__ texts. |
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The task is a __multi-label classification__ with the following labels: |
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```yaml |
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0: no_emotion |
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1: joy |
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2: sadness |
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3: surprise |
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4: fear |
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5: anger |
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``` |
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Label to Russian label: |
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```yaml |
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no_emotion: нет эмоции |
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joy: радость |
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sadness: грусть |
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surprise: удивление |
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fear: страх |
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anger: злость |
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``` |
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## Usage |
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```python |
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from transformers import pipeline |
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model = pipeline(model="seara/rubert-base-cased-cedr-russian-emotion") |
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model("Привет, ты мне нравишься!") |
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# [{'label': 'joy', 'score': 0.9388909935951233}] |
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``` |
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## Dataset |
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This model was trained on [CEDR dataset](https://huggingface.co/datasets/cedr). |
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An overview of the training data can be found in it's [Hugging Face card](https://huggingface.co/datasets/cedr) |
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or in the source [article](https://www.sciencedirect.com/science/article/pii/S1877050921013247). |
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## Training |
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Training were done in this [project](https://github.com/searayeah/bert-russian-sentiment-emotion) with this parameters: |
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```yaml |
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tokenizer.max_length: null |
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batch_size: 64 |
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optimizer: adam |
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lr: 0.00001 |
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weight_decay: 0 |
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num_epochs: 5 |
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``` |
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## Eval results (on test split) |
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| |no_emotion|joy |sadness|surprise|fear |anger|micro avg|macro avg|weighted avg| |
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|---------|----------|------|-------|--------|-------|-----|---------|---------|------------| |
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|precision|0.87 |0.84 |0.85 |0.74 |0.7 |0.66 |0.83 |0.78 |0.83 | |
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|recall |0.84 |0.86 |0.82 |0.71 |0.74 |0.33 |0.79 |0.72 |0.79 | |
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|f1-score |0.86 |0.85 |0.84 |0.72 |0.72 |0.44 |0.81 |0.74 |0.8 | |
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|auc-roc |0.95 |0.97 |0.96 |0.94 |0.93 |0.86 |0.95 |0.93 |0.95 | |
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|support |734 |353 |379 |170 |141 |125 |1902 |1902 |1902 | |
