Text Classification
Transformers
Safetensors
bert
numberwang
character-level
multilingual
tiny
Eval Results (legacy)
text-embeddings-inference
Instructions to use graafhenk/numberwang with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use graafhenk/numberwang with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="graafhenk/numberwang")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("graafhenk/numberwang") model = AutoModelForSequenceClassification.from_pretrained("graafhenk/numberwang", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from graafhenk/numberwang: direct link, hf CLI and curl.
- Browser
- Download file 230 Bytes
-
https://huggingface.co/graafhenk/numberwang/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://graafhenk/numberwang/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/graafhenk/numberwang/resolve/main/tokenizer_config.json
230 Bytes
| { | |
| "backend": "tokenizers", | |
| "cls_token": "[CLS]", | |
| "mask_token": "[MASK]", | |
| "model_max_length": 64, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "tokenizer_class": "TokenizersBackend", | |
| "unk_token": "[UNK]" | |
| } | |