Token Classification
Transformers
Safetensors
Ukrainian
roberta
ner
named-entity-recognition
pii
pii-detection
personal-data
anonymization
pseudonymization
de-identification
data-privacy
privacy
redaction
legal
legal-nlp
court-decisions
ukrainian
ukraine
synthetic-data
Eval Results (legacy)
Instructions to use davidmelash/wechsel_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use davidmelash/wechsel_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="davidmelash/wechsel_base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("davidmelash/wechsel_base") model = AutoModelForTokenClassification.from_pretrained("davidmelash/wechsel_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from davidmelash/wechsel_base: direct link, hf CLI and curl.
- Browser
- Download file 388 Bytes
-
https://huggingface.co/davidmelash/wechsel_base/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://davidmelash/wechsel_base/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/davidmelash/wechsel_base/resolve/main/tokenizer_config.json
388 Bytes
| { | |
| "add_prefix_space": false, | |
| "backend": "tokenizers", | |
| "bos_token": "<s>", | |
| "cls_token": "<s>", | |
| "eos_token": "</s>", | |
| "errors": "replace", | |
| "is_local": false, | |
| "local_files_only": false, | |
| "mask_token": "<mask>", | |
| "model_max_length": 512, | |
| "pad_token": "<pad>", | |
| "sep_token": "</s>", | |
| "tokenizer_class": "RobertaTokenizer", | |
| "trim_offsets": true, | |
| "unk_token": "<unk>" | |
| } | |