Update README.md

README.md

@@ -1,3 +1,83 @@
 ---
+
 license: mit
+language: ["ru"]
+tags:
+- russian
+- classification
+- emotion
+- emotion-detection
+- emotion-recognition
+- multiclass
+widget:
+- text: "Как дела?"
+- text: "Дурак твой дед"
+- text: "Только попробуй!!!"
+- text: "Не хочу в школу("
+- text: "Сейчас ровно час дня"
+- text: "А ты уверен, что эти полоски снизу не врут? Точно уверен? Вот прям 100 процентов?"
+datasets:
+- Djacon/ru_goemotions
+
 ---
+
+# First - you should prepare few functions to talk to model
+
+```python
+import torch
+from transformers import BertForSequenceClassification, AutoTokenizer
+
+LABELS = ['радость', 'интерес', 'удивление', 'печаль', 'гнев', 'отвращение', 'страх', 'вина', 'нейтрально']
+tokenizer = AutoTokenizer.from_pretrained('Djacon/rubert-tiny2-russian-emotion-detection')
+model = BertForSequenceClassification.from_pretrained('Djacon/rubert-tiny2-russian-emotion-detection')
+
+# Predicting emotion in text
+@torch.no_grad()
+def predict_emotion(text: str) -> str:
+    inputs = tokenizer(text, truncation=True, return_tensors='pt')
+    inputs = inputs.to(model.device)
+
+    outputs = model(**inputs)
+
+    pred = torch.nn.functional.softmax(outputs.logits, dim=1)
+    pred = pred.argmax(dim=1)
+
+    return LABELS[pred[0]]
+
+# Probabilistic prediction of emotion in a text
+@torch.no_grad()
+def predict_emotions(text: str) -> list:
+    inputs = tokenizer(text, truncation=True, return_tensors='pt')
+    inputs = inputs.to(model.device)
+
+    outputs = model(**inputs)
+
+    pred = torch.nn.functional.softmax(outputs.logits, dim=1)
+
+    emotions_list = {}
+    for i in range(len(pred[0].tolist())):
+        emotions_list[LABELS[i]] = round(pred[0].tolist()[i], 4)
+    return emotions_list
+```
+
+# And then - just gently ask a model to predict your emotion
+
+```python
+simple_prediction = predict_emotion("Какой же сегодня прекрасный день, братья")
+not_simple_prediction = predict_emotions("Какой же сегодня прекрасный день, братья")
+
+print(simple_prediction)
+print(not_simple_prediction)
+# happiness
+# {'neutral': 0.0004941817605867982, 'happiness': 0.9979524612426758, 'sadness': 0.0002536600804887712, 'enthusiasm': 0.0005498139653354883, 'fear': 0.00025326196919195354, 'anger': 0.0003583927755244076, 'disgust': 0.00013807788491249084}
+```
+
+# Citations
+```
+@misc{Djacon,
+  author = {Djacon},
+  year = {2023},
+  publisher = {Hugging Face},
+  journal = {Hugging Face Hub},
+}
+```