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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-class-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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- russian |
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- multiclass |
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- classification |
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datasets: |
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- sismetanin/rureviews |
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- RuSentiment |
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- LinisCrowd2015 |
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- LinisCrowd2016 |
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- KaggleRussianNews |
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--- |
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This is [RuBERT](https://huggingface.co/DeepPavlov/rubert-base-cased) model fine-tuned for __sentiment classification__ of short __Russian__ texts. |
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The task is a __multi-class classification__ with the following labels: |
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```yaml |
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0: neutral |
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1: positive |
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2: negative |
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``` |
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Label to Russian label: |
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```yaml |
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neutral: нейтральный |
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positive: позитивный |
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negative: негативный |
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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-russian-sentiment") |
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model("Привет, ты мне нравишься!") |
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# [{'label': 'positive', 'score': 0.9818321466445923}] |
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``` |
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## Dataset |
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This model was trained on the union of the following datasets: |
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- Kaggle Russian News Dataset |
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- Linis Crowd 2015 |
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- Linis Crowd 2016 |
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- RuReviews |
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- RuSentiment |
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An overview of the training data can be found on [S. Smetanin Github repository](https://github.com/sismetanin/sentiment-analysis-in-russian). |
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__Download links for all Russian sentiment datasets collected by Smetanin can be found in this [repository](https://github.com/searayeah/russian-sentiment-emotion-datasets).__ |
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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: 256 |
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batch_size: 32 |
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optimizer: adam |
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lr: 0.00001 |
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weight_decay: 0 |
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epochs: 2 |
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``` |
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Train/validation/test splits are 80%/10%/10%. |
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## Eval results (on test split) |
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| |neutral|positive|negative|macro avg|weighted avg| |
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|---------|-------|--------|--------|---------|------------| |
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|precision|0.72 |0.85 |0.75 |0.77 |0.77 | |
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|recall |0.75 |0.84 |0.72 |0.77 |0.77 | |
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|f1-score |0.73 |0.84 |0.73 |0.77 |0.77 | |
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|auc-roc |0.86 |0.96 |0.92 |0.91 |0.91 | |
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|support |5196 |3831 |3599 |12626 |12626 | |
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