Zero-Shot Classification
Transformers
PyTorch
Safetensors
Russian
bert
text-classification
rubert
russian
nli
rte
Instructions to use cointegrated/rubert-tiny-bilingual-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cointegrated/rubert-tiny-bilingual-nli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="cointegrated/rubert-tiny-bilingual-nli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cointegrated/rubert-tiny-bilingual-nli") model = AutoModelForSequenceClassification.from_pretrained("cointegrated/rubert-tiny-bilingual-nli", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from cointegrated/rubert-tiny-bilingual-nli: direct link, hf CLI and curl.
- Browser
- Download file 394 Bytes
-
https://huggingface.co/cointegrated/rubert-tiny-bilingual-nli/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://cointegrated/rubert-tiny-bilingual-nli/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/cointegrated/rubert-tiny-bilingual-nli/resolve/main/tokenizer_config.json
394 Bytes
| {"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, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "/gd/MyDrive/models/rubert-tiny-nli-twoway", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer"} |