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--- |
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license: mit |
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language: ["ru"] |
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tags: |
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- russian |
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- classification |
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- emotion |
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- emotion-detection |
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- emotion-recognition |
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- multiclass |
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widget: |
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- text: "Как дела?" |
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- text: "Дурак твой дед" |
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- text: "Только попробуй!!!" |
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- text: "Не хочу в школу(" |
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- text: "Сейчас ровно час дня" |
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- text: "А ты уверен, что эти полоски снизу не врут? Точно уверен? Вот прям 100 процентов?" |
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datasets: |
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- Djacon/ru_goemotions |
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--- |
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# First - you should prepare few functions to talk to model |
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```python |
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import torch |
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from transformers import BertForSequenceClassification, AutoTokenizer |
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LABELS = ['радость', 'интерес', 'удивление', 'печаль', 'гнев', 'отвращение', 'страх', 'вина', 'нейтрально'] |
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tokenizer = AutoTokenizer.from_pretrained('Djacon/rubert-tiny2-russian-emotion-detection') |
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model = BertForSequenceClassification.from_pretrained('Djacon/rubert-tiny2-russian-emotion-detection') |
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# Predicting emotion in text |
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@torch.no_grad() |
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def predict_emotion(text: str) -> str: |
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inputs = tokenizer(text, truncation=True, return_tensors='pt') |
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inputs = inputs.to(model.device) |
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outputs = model(**inputs) |
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pred = torch.nn.functional.softmax(outputs.logits, dim=1) |
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pred = pred.argmax(dim=1) |
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return LABELS[pred[0]] |
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# Probabilistic prediction of emotion in a text |
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@torch.no_grad() |
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def predict_emotions(text: str) -> list: |
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inputs = tokenizer(text, truncation=True, return_tensors='pt') |
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inputs = inputs.to(model.device) |
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outputs = model(**inputs) |
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pred = torch.nn.functional.softmax(outputs.logits, dim=1) |
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emotions_list = {} |
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for i in range(len(pred[0].tolist())): |
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emotions_list[LABELS[i]] = round(pred[0].tolist()[i], 4) |
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return emotions_list |
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``` |
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# And then - just gently ask a model to predict your emotion |
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```python |
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simple_prediction = predict_emotion("Какой же сегодня прекрасный день, братья") |
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not_simple_prediction = predict_emotions("Какой же сегодня прекрасный день, братья") |
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print(simple_prediction) |
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print(not_simple_prediction) |
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# happiness |
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# {'neutral': 0.0004941817605867982, 'happiness': 0.9979524612426758, 'sadness': 0.0002536600804887712, 'enthusiasm': 0.0005498139653354883, 'fear': 0.00025326196919195354, 'anger': 0.0003583927755244076, 'disgust': 0.00013807788491249084} |
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``` |
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# Citations |
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``` |
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@misc{Djacon, |
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author = {Djacon}, |
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year = {2023}, |
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publisher = {Hugging Face}, |
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journal = {Hugging Face Hub}, |
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} |
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``` |
