# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("MonoHime/rubert_conversational_cased_sentiment", device_map="auto")Quick Links
Keras model with ruBERT conversational embedder for Sentiment Analysis
Russian texts sentiment classification.
Model trained on Tatyana/ru_sentiment_dataset
Labels meaning
0: NEUTRAL
1: POSITIVE
2: NEGATIVE
How to use
!pip install tensorflow-gpu
!pip install deeppavlov
!python -m deeppavlov install squad_bert
!pip install fasttext
!pip install transformers
!python -m deeppavlov install bert_sentence_embedder
from deeppavlov import build_model
model = build_model(Tatyana/rubert_conversational_cased_sentiment/custom_config.json)
model(["Сегодня хорошая погода", "Я счастлив проводить с тобою время", "Мне нравится эта музыкальная композиция"])
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MonoHime/rubert_conversational_cased_sentiment")