Text Classification
Transformers
PyTorch
Russian
bert
sentiment-analysis
multi-class-classification
sentiment analysis
rubert
sentiment
russian
multiclass
classification
text-embeddings-inference
Instructions to use r1char9/rubert-base-cased-russian-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use r1char9/rubert-base-cased-russian-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="r1char9/rubert-base-cased-russian-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("r1char9/rubert-base-cased-russian-sentiment") model = AutoModelForSequenceClassification.from_pretrained("r1char9/rubert-base-cased-russian-sentiment", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#3 opened over 1 year ago
by
SFconvertbot
Adding `safetensors` variant of this model
#2 opened over 1 year ago
by
SFconvertbot
Adding `safetensors` variant of this model
#1 opened almost 2 years ago
by
SFconvertbot