Text Classification
Transformers
PyTorch
Safetensors
English
deberta-v2
Trained with AutoTrain
text-embeddings-inference
Instructions to use Showroom/accessories_subcategory_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Showroom/accessories_subcategory_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Showroom/accessories_subcategory_classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Showroom/accessories_subcategory_classifier") model = AutoModelForSequenceClassification.from_pretrained("Showroom/accessories_subcategory_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from Showroom/accessories_subcategory_classifier: direct link, hf CLI and curl.
- Browser
- Download file 412 Bytes
-
https://huggingface.co/Showroom/accessories_subcategory_classifier/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Showroom/accessories_subcategory_classifier/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Showroom/accessories_subcategory_classifier/resolve/main/tokenizer_config.json
412 Bytes
| { | |
| "bos_token": "[CLS]", | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "do_lower_case": false, | |
| "eos_token": "[SEP]", | |
| "mask_token": "[MASK]", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "sp_model_kwargs": {}, | |
| "split_by_punct": false, | |
| "tokenizer_class": "DebertaV2Tokenizer", | |
| "unk_token": "[UNK]", | |
| "vocab_type": "spm" | |
| } | |