Text Classification
Transformers
PyTorch
Safetensors
Russian
bert
russian
classification
sentiment
emotion-classification
multiclass
text-embeddings-inference
Instructions to use cointegrated/rubert-tiny2-cedr-emotion-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cointegrated/rubert-tiny2-cedr-emotion-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cointegrated/rubert-tiny2-cedr-emotion-detection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cointegrated/rubert-tiny2-cedr-emotion-detection") model = AutoModelForSequenceClassification.from_pretrained("cointegrated/rubert-tiny2-cedr-emotion-detection") - Inference
- Notebooks
- Google Colab
- Kaggle
| {"do_lower_case": false, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "do_basic_tokenize": true, "never_split": null, "model_max_length": 2048, "special_tokens_map_file": null, "name_or_path": "cointegrated/rubert-tiny2", "tokenizer_class": "BertTokenizer"} |