Token Classification
Transformers
PyTorch
English
deberta-v2
NER
token classification
information extraction
question answering
Instructions to use knowledgator/UTC-DeBERTA-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use knowledgator/UTC-DeBERTA-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="knowledgator/UTC-DeBERTA-base")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("knowledgator/UTC-DeBERTA-base") model = AutoModelForTokenClassification.from_pretrained("knowledgator/UTC-DeBERTA-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download special_tokens_map.json from knowledgator/UTC-DeBERTA-base: direct link, hf CLI and curl.
- Browser
- Download file 173 Bytes
-
https://huggingface.co/knowledgator/UTC-DeBERTA-base/resolve/main/special_tokens_map.json
- Command line
-
hf download hf://knowledgator/UTC-DeBERTA-base/special_tokens_map.json
-
curl -L -o special_tokens_map.json https://huggingface.co/knowledgator/UTC-DeBERTA-base/resolve/main/special_tokens_map.json
173 Bytes
| { | |
| "bos_token": "[CLS]", | |
| "cls_token": "[CLS]", | |
| "eos_token": "[SEP]", | |
| "mask_token": "[MASK]", | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "unk_token": "[UNK]" | |
| } | |