Text Classification
Transformers
Safetensors
Russian
bert
russian
sentiment-analysis
multi-class-classification
rubert
tiny
text-embeddings-inference
Instructions to use sergeyzh/rubert-tiny-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sergeyzh/rubert-tiny-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sergeyzh/rubert-tiny-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sergeyzh/rubert-tiny-sentiment") model = AutoModelForSequenceClassification.from_pretrained("sergeyzh/rubert-tiny-sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "sergeyzh/rubert-tiny-sentiment", | |
| "architectures": [ | |
| "BertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "emb_size": 312, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 312, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 600, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 2048, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 3, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "transformers_version": "4.57.6", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 83828, | |
| "num_labels": 3, | |
| "id2label": { | |
| "0": "Negative", | |
| "1": "Neutral", | |
| "2": "Positive" | |
| }, | |
| "label2id": { | |
| "Negative": 0, | |
| "Neutral": 1, | |
| "Positive": 2 | |
| } | |
| } |